Machine Learning Engineer Nanodegree¶
Reinforcement Learning¶
Project: Train a Smartcab to Drive¶
Welcome to the fourth project of the Machine Learning Engineer Nanodegree! In this notebook, template code has already been provided for you to aid in your analysis of the Smartcab and your implemented learning algorithm. You will not need to modify the included code beyond what is requested. There will be questions that you must answer which relate to the project and the visualizations provided in the notebook. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide in agent.py.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.
Getting Started¶
In this project, you will work towards constructing an optimized Q-Learning driving agent that will navigate a Smartcab through its environment towards a goal. Since the Smartcab is expected to drive passengers from one location to another, the driving agent will be evaluated on two very important metrics: Safety and Reliability. A driving agent that gets the Smartcab to its destination while running red lights or narrowly avoiding accidents would be considered unsafe. Similarly, a driving agent that frequently fails to reach the destination in time would be considered unreliable. Maximizing the driving agent's safety and reliability would ensure that Smartcabs have a permanent place in the transportation industry.
Safety and Reliability are measured using a letter-grade system as follows:
| Grade | Safety | Reliability |
|---|---|---|
| A+ | Agent commits no traffic violations, and always chooses the correct action. |
Agent reaches the destination in time for 100% of trips. |
| A | Agent commits few minor traffic violations, such as failing to move on a green light. |
Agent reaches the destination on time for at least 90% of trips. |
| B | Agent commits frequent minor traffic violations, such as failing to move on a green light. |
Agent reaches the destination on time for at least 80% of trips. |
| C | Agent commits at least one major traffic violation, such as driving through a red light. |
Agent reaches the destination on time for at least 70% of trips. |
| D | Agent causes at least one minor accident, such as turning left on green with oncoming traffic. |
Agent reaches the destination on time for at least 60% of trips. |
| F | Agent causes at least one major accident, such as driving through a red light with cross-traffic. |
Agent fails to reach the destination on time for at least 60% of trips. |
To assist evaluating these important metrics, you will need to load visualization code that will be used later on in the project. Run the code cell below to import this code which is required for your analysis.
In [1]:
# Import the visualization code
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
Understand the World¶
Before starting to work on implementing your driving agent, it's necessary to first understand the world (environment) which the Smartcab and driving agent work in. One of the major components to building a self-learning agent is understanding the characteristics about the agent, which includes how the agent operates. To begin, simply run the agent.py agent code exactly how it is -- no need to make any additions whatsoever. Let the resulting simulation run for some time to see the various working components. Note that in the visual simulation (if enabled), the white vehicle is the Smartcab.
Question 1¶
In a few sentences, describe what you observe during the simulation when running the default agent.py agent code. Some things you could consider:
- Does the Smartcab move at all during the simulation?
- What kind of rewards is the driving agent receiving?
- How does the light changing color affect the rewards?
Hint: From the /smartcab/ top-level directory (where this notebook is located), run the command
'python smartcab/agent.py'
In [2]:
!python smartcab/agent.py
/------------------------- | Training trial 1 \------------------------- Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.82) Agent not enforced to meet deadline. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.17) Agent not enforced to meet deadline. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.30) Agent not enforced to meet deadline. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.01) Agent not enforced to meet deadline. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.44) Agent not enforced to meet deadline. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.37) Agent not enforced to meet deadline. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.04) Agent not enforced to meet deadline. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.24) Agent not enforced to meet deadline. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.40) Agent not enforced to meet deadline. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.24) Agent not enforced to meet deadline. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.09) Agent not enforced to meet deadline. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.14) Agent not enforced to meet deadline. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.83) Agent not enforced to meet deadline. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) Agent not enforced to meet deadline. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.32) Agent not enforced to meet deadline. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.06) Agent not enforced to meet deadline. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.31) Agent not enforced to meet deadline. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.82) Agent not enforced to meet deadline. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.32) Agent not enforced to meet deadline. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.95) Agent not enforced to meet deadline. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.32) Agent not enforced to meet deadline. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.75) Agent not enforced to meet deadline. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.26) Agent not enforced to meet deadline. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) Agent not enforced to meet deadline. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.05) Agent not enforced to meet deadline. /------------------- | Step 25 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.01) Agent not enforced to meet deadline. /------------------- | Step 26 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.55) Agent not enforced to meet deadline. /------------------- | Step 27 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.90) Agent not enforced to meet deadline. /------------------- | Step 28 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.14) Agent not enforced to meet deadline. /------------------- | Step 29 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.35) Agent not enforced to meet deadline. /------------------- | Step 30 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.13) Agent not enforced to meet deadline. /------------------- | Step 31 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.42) Agent not enforced to meet deadline. /------------------- | Step 32 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.23) Agent not enforced to meet deadline. /------------------- | Step 33 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.15) Agent not enforced to meet deadline. /------------------- | Step 34 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.51) Agent not enforced to meet deadline. /------------------- | Step 35 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.33) Agent not enforced to meet deadline. /------------------- | Step 36 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.96) Agent not enforced to meet deadline. /------------------- | Step 37 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.80) Agent not enforced to meet deadline. /------------------- | Step 38 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.71) Agent not enforced to meet deadline. /------------------- | Step 39 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.67) Agent not enforced to meet deadline. /------------------- | Step 40 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.16) Agent not enforced to meet deadline. /------------------- | Step 41 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.79) Agent not enforced to meet deadline. /------------------- | Step 42 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.06) Agent not enforced to meet deadline. /------------------- | Step 43 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.31) Agent not enforced to meet deadline. /------------------- | Step 44 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.62) Agent not enforced to meet deadline. /------------------- | Step 45 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.06) Agent not enforced to meet deadline. /------------------- | Step 46 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.32) Agent not enforced to meet deadline. /------------------- | Step 47 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.31) Agent not enforced to meet deadline. /------------------- | Step 48 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.11) Agent not enforced to meet deadline. /------------------- | Step 49 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.81) Agent not enforced to meet deadline. /------------------- | Step 50 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.81) Agent not enforced to meet deadline. /------------------- | Step 51 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.86) Agent not enforced to meet deadline. /------------------- | Step 52 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) Agent not enforced to meet deadline. /------------------- | Step 53 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.36) Agent not enforced to meet deadline. /------------------- | Step 54 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.88) Agent not enforced to meet deadline. /------------------- | Step 55 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.45) Agent not enforced to meet deadline. /------------------- | Step 56 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.19) Agent not enforced to meet deadline. /------------------- | Step 57 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.68) Agent not enforced to meet deadline. /------------------- | Step 58 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.90) Agent not enforced to meet deadline. /------------------- | Step 59 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.37) Agent not enforced to meet deadline. /------------------- | Step 60 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.15) Agent not enforced to meet deadline. /------------------- | Step 61 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.84) Agent not enforced to meet deadline. /------------------- | Step 62 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.95) Agent not enforced to meet deadline. /------------------- | Step 63 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.68) Agent not enforced to meet deadline. /------------------- | Step 64 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.58) Agent not enforced to meet deadline. /------------------- | Step 65 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.39) Agent not enforced to meet deadline. /------------------- | Step 66 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.47) Agent not enforced to meet deadline. /------------------- | Step 67 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.98) Agent not enforced to meet deadline. /------------------- | Step 68 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.99) Agent not enforced to meet deadline. /------------------- | Step 69 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.73) Agent not enforced to meet deadline. /------------------- | Step 70 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.52) Agent not enforced to meet deadline. /------------------- | Step 71 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.96) Agent not enforced to meet deadline. /------------------- | Step 72 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.00) Agent not enforced to meet deadline. /------------------- | Step 73 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.80) Agent not enforced to meet deadline. /------------------- | Step 74 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.35) Agent not enforced to meet deadline. /------------------- | Step 75 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.29) Agent not enforced to meet deadline. /------------------- | Step 76 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.76) Agent not enforced to meet deadline. /------------------- | Step 77 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.57) Agent not enforced to meet deadline. /------------------- | Step 78 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.78) Agent not enforced to meet deadline. /------------------- | Step 79 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.39) Agent not enforced to meet deadline. /------------------- | Step 80 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.65) Agent not enforced to meet deadline. /------------------- | Step 81 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.78) Agent not enforced to meet deadline. /------------------- | Step 82 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.33) Agent not enforced to meet deadline. /------------------- | Step 83 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.42) Agent not enforced to meet deadline. /------------------- | Step 84 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.66) Agent not enforced to meet deadline. /------------------- | Step 85 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.79) Agent not enforced to meet deadline. /------------------- | Step 86 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.06) Agent not enforced to meet deadline. /------------------- | Step 87 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.69) Agent not enforced to meet deadline. /------------------- | Step 88 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.82) Agent not enforced to meet deadline. /------------------- | Step 89 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.01) Agent not enforced to meet deadline. /------------------- | Step 90 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.41) Agent not enforced to meet deadline. /------------------- | Step 91 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.04) Agent not enforced to meet deadline. /------------------- | Step 92 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.01) Agent not enforced to meet deadline. /------------------- | Step 93 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.41) Agent not enforced to meet deadline. /------------------- | Step 94 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.19) Agent not enforced to meet deadline. /------------------- | Step 95 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.98) Agent not enforced to meet deadline. /------------------- | Step 96 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.63) Agent not enforced to meet deadline. /------------------- | Step 97 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.57) Agent not enforced to meet deadline. /------------------- | Step 98 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.78) Agent not enforced to meet deadline. /------------------- | Step 99 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.96) Agent not enforced to meet deadline. /------------------- | Step 100 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.05) Agent not enforced to meet deadline. /------------------- | Step 101 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.70) Agent not enforced to meet deadline. /------------------- | Step 102 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.10) Agent not enforced to meet deadline. /------------------- | Step 103 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.02) Agent not enforced to meet deadline. /------------------- | Step 104 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.32) Agent not enforced to meet deadline. /------------------- | Step 105 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.79) Agent not enforced to meet deadline. /------------------- | Step 106 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.61) Agent not enforced to meet deadline. /------------------- | Step 107 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.58) Agent not enforced to meet deadline. /------------------- | Step 108 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.34) Agent not enforced to meet deadline. /------------------- | Step 109 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.25) Agent not enforced to meet deadline. /------------------- | Step 110 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) Agent not enforced to meet deadline. /------------------- | Step 111 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.71) Agent not enforced to meet deadline. /------------------- | Step 112 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.14) Agent not enforced to meet deadline. /------------------- | Step 113 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.98) Agent not enforced to meet deadline. /------------------- | Step 114 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.47) Agent not enforced to meet deadline. /------------------- | Step 115 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.53) Agent not enforced to meet deadline. /------------------- | Step 116 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.64) Agent not enforced to meet deadline. /------------------- | Step 117 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.25) Agent not enforced to meet deadline. /------------------- | Step 118 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.02) Agent not enforced to meet deadline. /------------------- | Step 119 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.83) Agent not enforced to meet deadline. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 2 \------------------------- Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.83) Agent not enforced to meet deadline. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.26) Agent not enforced to meet deadline. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.74) Agent not enforced to meet deadline. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.68) Agent not enforced to meet deadline. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.20) Agent not enforced to meet deadline. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.99) Agent not enforced to meet deadline. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.90) Agent not enforced to meet deadline. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.78) Agent not enforced to meet deadline. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.32) Agent not enforced to meet deadline. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 3.00) Agent not enforced to meet deadline. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.76) Agent not enforced to meet deadline. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.35) Agent not enforced to meet deadline. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.41) Agent not enforced to meet deadline. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.82) Agent not enforced to meet deadline. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.70) Agent not enforced to meet deadline. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.78) Agent not enforced to meet deadline. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.40) Agent not enforced to meet deadline. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.78) Agent not enforced to meet deadline. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.59) Agent not enforced to meet deadline. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.90) Agent not enforced to meet deadline. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.10) Agent not enforced to meet deadline. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.20) Agent not enforced to meet deadline. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.52) Agent not enforced to meet deadline. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.78) Agent not enforced to meet deadline. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.35) Agent not enforced to meet deadline. /------------------- | Step 25 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.61) Agent not enforced to meet deadline. /------------------- | Step 26 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.95) Agent not enforced to meet deadline. /------------------- | Step 27 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.42) Agent not enforced to meet deadline. /------------------- | Step 28 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.21) Agent not enforced to meet deadline. /------------------- | Step 29 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.53) Agent not enforced to meet deadline. /------------------- | Step 30 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.06) Agent not enforced to meet deadline. /------------------- | Step 31 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.12) Agent not enforced to meet deadline. /------------------- | Step 32 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.98) Agent not enforced to meet deadline. /------------------- | Step 33 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.72) Agent not enforced to meet deadline. /------------------- | Step 34 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.77) Agent not enforced to meet deadline. /------------------- | Step 35 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.90) Agent not enforced to meet deadline. /------------------- | Step 36 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.99) Agent not enforced to meet deadline. /------------------- | Step 37 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.15) Agent not enforced to meet deadline. /------------------- | Step 38 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.48) Agent not enforced to meet deadline. /------------------- | Step 39 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.76) Agent not enforced to meet deadline. /------------------- | Step 40 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.55) Agent not enforced to meet deadline. /------------------- | Step 41 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.51) Agent not enforced to meet deadline. /------------------- | Step 42 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.63) Agent not enforced to meet deadline. /------------------- | Step 43 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.14) Agent not enforced to meet deadline. /------------------- | Step 44 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.48) Agent not enforced to meet deadline. /------------------- | Step 45 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.67) Agent not enforced to meet deadline. /------------------- | Step 46 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.03) Agent not enforced to meet deadline. /------------------- | Step 47 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.54) Agent not enforced to meet deadline. /------------------- | Step 48 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.21) Agent not enforced to meet deadline. /------------------- | Step 49 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.11) Agent not enforced to meet deadline. /------------------- | Step 50 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.07) Agent not enforced to meet deadline. /------------------- | Step 51 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.77) Agent not enforced to meet deadline. /------------------- | Step 52 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.47) Agent not enforced to meet deadline. /------------------- | Step 53 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.52) Agent not enforced to meet deadline. /------------------- | Step 54 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.61) Agent not enforced to meet deadline. /------------------- | Step 55 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.67) Agent not enforced to meet deadline. /------------------- | Step 56 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.44) Agent not enforced to meet deadline. /------------------- | Step 57 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.11) Agent not enforced to meet deadline. /------------------- | Step 58 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.01) Agent not enforced to meet deadline. /------------------- | Step 59 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.20) Agent not enforced to meet deadline. /------------------- | Step 60 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.57) Agent not enforced to meet deadline. /------------------- | Step 61 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.10) Agent not enforced to meet deadline. /------------------- | Step 62 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.01) Agent not enforced to meet deadline. /------------------- | Step 63 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.12) Agent not enforced to meet deadline. /------------------- | Step 64 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.34) Agent not enforced to meet deadline. /------------------- | Step 65 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.11) Agent not enforced to meet deadline. /------------------- | Step 66 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.57) Agent not enforced to meet deadline. /------------------- | Step 67 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.86) Agent not enforced to meet deadline. /------------------- | Step 68 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.58) Agent not enforced to meet deadline. /------------------- | Step 69 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.74) Agent not enforced to meet deadline. /------------------- | Step 70 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.87) Agent not enforced to meet deadline. /------------------- | Step 71 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.32) Agent not enforced to meet deadline. /------------------- | Step 72 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.12) Agent not enforced to meet deadline. /------------------- | Step 73 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.78) Agent not enforced to meet deadline. /------------------- | Step 74 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.35) Agent not enforced to meet deadline. /------------------- | Step 75 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.66) Agent not enforced to meet deadline. /------------------- | Step 76 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.97) Agent not enforced to meet deadline. /------------------- | Step 77 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.57) Agent not enforced to meet deadline. /------------------- | Step 78 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.01) Agent not enforced to meet deadline. /------------------- | Step 79 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.78) Agent not enforced to meet deadline. /------------------- | Step 80 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.51) Agent not enforced to meet deadline. /------------------- | Step 81 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.84) Agent not enforced to meet deadline. /------------------- | Step 82 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.25) Agent not enforced to meet deadline. /------------------- | Step 83 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.61) Agent not enforced to meet deadline. /------------------- | Step 84 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.73) Agent not enforced to meet deadline. /------------------- | Step 85 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.52) Agent not enforced to meet deadline. /------------------- | Step 86 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.16) Agent not enforced to meet deadline. /------------------- | Step 87 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.42) Agent not enforced to meet deadline. /------------------- | Step 88 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.68) Agent not enforced to meet deadline. /------------------- | Step 89 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.68) Agent not enforced to meet deadline. /------------------- | Step 90 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.41) Agent not enforced to meet deadline. /------------------- | Step 91 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.43) Agent not enforced to meet deadline. /------------------- | Step 92 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) Agent not enforced to meet deadline. /------------------- | Step 93 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.49) Agent not enforced to meet deadline. /------------------- | Step 94 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.36) Agent not enforced to meet deadline. /------------------- | Step 95 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.77) Agent not enforced to meet deadline. /------------------- | Step 96 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.96) Agent not enforced to meet deadline. /------------------- | Step 97 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.88) Agent not enforced to meet deadline. /------------------- | Step 98 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.33) Agent not enforced to meet deadline. /------------------- | Step 99 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.41) Agent not enforced to meet deadline. /------------------- | Step 100 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.24) Agent not enforced to meet deadline. /------------------- | Step 101 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.56) Agent not enforced to meet deadline. /------------------- | Step 102 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -6.00) Agent not enforced to meet deadline. /------------------- | Step 103 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.93) Agent not enforced to meet deadline. /------------------- | Step 104 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.53) Agent not enforced to meet deadline. /------------------- | Step 105 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.82) Agent not enforced to meet deadline. /------------------- | Step 106 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.52) Agent not enforced to meet deadline. /------------------- | Step 107 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.72) Agent not enforced to meet deadline. /------------------- | Step 108 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.54) Agent not enforced to meet deadline. /------------------- | Step 109 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.06) Agent not enforced to meet deadline. /------------------- | Step 110 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.39) Agent not enforced to meet deadline. /------------------- | Step 111 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.00) Agent not enforced to meet deadline. /------------------- | Step 112 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.27) Agent not enforced to meet deadline. /------------------- | Step 113 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.69) Agent not enforced to meet deadline. /------------------- | Step 114 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.37) Agent not enforced to meet deadline. /------------------- | Step 115 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.31) Agent not enforced to meet deadline. /------------------- | Step 116 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.26) Agent not enforced to meet deadline. /------------------- | Step 117 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) Agent not enforced to meet deadline. /------------------- | Step 118 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.56) Agent not enforced to meet deadline. /------------------- | Step 119 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.17) Agent not enforced to meet deadline. /------------------- | Step 120 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.81) Agent not enforced to meet deadline. /------------------- | Step 121 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.38) Agent not enforced to meet deadline. /------------------- | Step 122 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.12) Agent not enforced to meet deadline. Simulation ended. . .
Answer:
The Smartcab does not move at all during the simulation. The agent was reciving positive or negative values as rewards for positive or negative behaviour respectively. The driving agent was receiving negative value as reward for idling at a green light when there was no oncoming traffic. It was receiving positive reward between 0 and 1 for idling at a green light when there was oncoming traffic. It was receiving positive reward greater than 1 for idling at a red light.
The agent is not forced to meet a deadline and it was not programmed to take any actions.
Understand the Code¶
In addition to understanding the world, it is also necessary to understand the code itself that governs how the world, simulation, and so on operate. Attempting to create a driving agent would be difficult without having at least explored the "hidden" devices that make everything work. In the /smartcab/ top-level directory, there are two folders: /logs/ (which will be used later) and /smartcab/. Open the /smartcab/ folder and explore each Python file included, then answer the following question.
Question 2¶
- In the
agent.pyPython file, choose three flags that can be set and explain how they change the simulation. - In the
environment.pyPython file, what Environment class function is called when an agent performs an action? - In the
simulator.pyPython file, what is the difference between the'render_text()'function and the'render()'function? - In the
planner.pyPython file, will the'next_waypoint()function consider the North-South or East-West direction first?
Answer:
1) alpha, the learning rate. If set to 0, no learning will take place. If set to 1, the agent will only learn and consider the most recent information. Since the environment is always changing we would like an alpha that starts strong and decays.
2) epsilon, the exploration rate. If set to less than 1, algorithm would choose uniformly from the set of actions other than the best action with probability 1-epsilon.
3) enforce_deadline, a flag to enforce the agent to reach the destination. If not set to true, the agent need not reach the destination as in our initial simulation above.
The function "act" is called when the agent performs an action.
render_text() displays information about the past trial in the command prompt or in the ipython notebook as above while render() displays the information of the current simulation in the GUI.
The function considers East-West direction first.
Implement a Basic Driving Agent¶
The first step to creating an optimized Q-Learning driving agent is getting the agent to actually take valid actions. In this case, a valid action is one of None, (do nothing) 'Left' (turn left), 'Right' (turn right), or 'Forward' (go forward). For your first implementation, navigate to the 'choose_action()' agent function and make the driving agent randomly choose one of these actions. Note that you have access to several class variables that will help you write this functionality, such as 'self.learning' and 'self.valid_actions'. Once implemented, run the agent file and simulation briefly to confirm that your driving agent is taking a random action each time step.
In [2]:
!python smartcab/agent.py
/------------------------- | Training trial 1 \------------------------- Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.18) Agent not enforced to meet deadline. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.85) Agent not enforced to meet deadline. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.36) Agent not enforced to meet deadline. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.86) Agent not enforced to meet deadline. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.83) Agent not enforced to meet deadline. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.16) Agent not enforced to meet deadline. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.82) Agent not enforced to meet deadline. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.91) Agent not enforced to meet deadline. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.11) Agent not enforced to meet deadline. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 2.07) Agent not enforced to meet deadline. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.55) Agent not enforced to meet deadline. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.31) Agent not enforced to meet deadline. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.30) Agent not enforced to meet deadline. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.75) Agent not enforced to meet deadline. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.97) Agent not enforced to meet deadline. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 2.98) Agent not enforced to meet deadline. Simulation ended. . .
Basic Agent Simulation Results¶
To obtain results from the initial simulation, you will need to adjust following flags:
'enforce_deadline'- Set this toTrueto force the driving agent to capture whether it reaches the destination in time.'update_delay'- Set this to a small value (such as0.01) to reduce the time between steps in each trial.'log_metrics'- Set this toTrueto log the simluation results as a.csvfile in/logs/.'n_test'- Set this to'10'to perform 10 testing trials.
Optionally, you may disable to the visual simulation (which can make the trials go faster) by setting the 'display' flag to False. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!
Once you have successfully completed the initial simulation (there should have been 20 training trials and 10 testing trials), run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded! Run the agent.py file after setting the flags from projects/smartcab folder instead of projects/smartcab/smartcab.
In [2]:
!python smartcab/agent.py
/------------------------- | Training trial 1 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.23) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.53) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.22) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.66) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.05) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 0.70) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.93) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.26) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.76) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 2.26) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.88) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.17) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.65) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.89) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.37) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.29) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.51) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 2 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.57) 97% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 93% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.27) 90% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.28) 87% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.98) 83% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.92) 80% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 77% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 73% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.91) 70% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.23) 67% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.64) 63% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.20) 60% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.94) 57% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 2.46) 53% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.89) 50% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.71) 47% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.33) 43% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.65) 40% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.45) 37% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 0.84) 33% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.80) 30% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.26) 27% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 0.50) 23% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.92) 20% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.71) 17% of time remaining to reach destination. /------------------- | Step 25 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.32) 13% of time remaining to reach destination. /------------------- | Step 26 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.00) 10% of time remaining to reach destination. /------------------- | Step 27 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.47) 7% of time remaining to reach destination. /------------------- | Step 28 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded -0.09) 3% of time remaining to reach destination. /------------------- | Step 29 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.94) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 3 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66) 96% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.69) 92% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.13) 88% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 84% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.63) 80% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 1.00) 76% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.89) 72% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.83) 68% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.73) 64% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.45) 60% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.45) 56% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.83) 52% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.33) 48% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.39) 44% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded -0.27) 40% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.43) 36% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.80) 32% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.67) 28% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.40) 24% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. !! Agent state not been updated! Agent drove left instead of forward. (rewarded -0.12) 20% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.14) 16% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.48) 12% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.41) 8% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.39) 4% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.82) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 4 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent drove left instead of forward. (rewarded 0.46) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.92) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.92) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.48) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.98) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.46) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.83) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.42) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.02) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.80) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.12) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.78) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.54) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.45) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.80) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.34) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.90) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.67) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.27) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 5 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.89) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.66) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.83) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.25) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.36) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.35) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.12) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.91) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.87) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.40) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.11) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.78) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.58) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded -0.16) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.57) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.24) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.54) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 1.15) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.39) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.68) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 6 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.66) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.64) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.57) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove left instead of forward. (rewarded 0.87) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.08) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.41) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.33) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.69) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.22) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.25) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.07) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.50) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.60) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.53) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.14) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.36) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.73) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.47) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.00) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 7 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.10) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.72) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.16) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.02) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.46) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.27) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.12) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.25) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.58) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.12) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.55) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.32) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.52) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.39) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.32) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.71) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.32) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.97) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.15) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.78) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 8 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.29) 96% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.90) 92% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.84) 88% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.69) 84% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove left instead of forward. (rewarded 0.26) 80% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.86) 76% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.43) 72% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.03) 68% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.60) 64% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.74) 60% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded -0.00) 56% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.78) 52% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.20) 48% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.12) 44% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.74) 40% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.95) 36% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.79) 32% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.78) 28% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.19) 24% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.83) 20% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.40) 16% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.19) 12% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 0.37) 8% of time remaining to reach destination. Trial Completed! Agent reached the destination. /------------------------- | Training trial 9 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.21) 97% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.87) 93% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.62) 90% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.07) 87% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.18) 83% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.77) 80% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.53) 77% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.89) 73% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.73) 70% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.33) 67% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.30) 63% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove left instead of forward. (rewarded 1.64) 60% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.50) 57% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.62) 53% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.30) 50% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.14) 47% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.52) 43% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 1.43) 40% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded -0.19) 37% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.70) 33% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.48) 30% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.04) 27% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 23% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.73) 20% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.31) 17% of time remaining to reach destination. /------------------- | Step 25 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 0.92) 13% of time remaining to reach destination. /------------------- | Step 26 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.10) 10% of time remaining to reach destination. /------------------- | Step 27 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.71) 7% of time remaining to reach destination. /------------------- | Step 28 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.08) 3% of time remaining to reach destination. /------------------- | Step 29 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.22) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 10 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.35) 96% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.32) 92% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.34) 88% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.93) 84% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.47) 80% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.34) 76% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.35) 72% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.92) 68% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.75) 64% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.75) 60% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.03) 56% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded -0.14) 52% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.85) 48% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.03) 44% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.26) 40% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.76) 36% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.25) 32% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.32) 28% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.03) 24% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.35) 20% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.08) 16% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.29) 12% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.11) 8% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.12) 4% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.92) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 11 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.77) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.08) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.73) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.02) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.80) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.89) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.19) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.93) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.27) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.71) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.53) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.07) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.00) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.62) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.50) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.34) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.69) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.76) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.40) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.22) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 12 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.50) 97% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.25) 93% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.12) 90% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.69) 87% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.03) 83% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.39) 80% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.53) 77% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 1.52) 73% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.44) 70% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.91) 67% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 1.70) 63% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.89) 60% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.65) 57% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.85) 53% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.42) 50% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.92) 47% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.38) 43% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.18) 40% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.42) 37% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.93) 33% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.16) 30% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.44) 27% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.74) 23% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.70) 20% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.39) 17% of time remaining to reach destination. /------------------- | Step 25 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.08) 13% of time remaining to reach destination. /------------------- | Step 26 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.73) 10% of time remaining to reach destination. /------------------- | Step 27 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.70) 7% of time remaining to reach destination. /------------------- | Step 28 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded -0.55) 3% of time remaining to reach destination. /------------------- | Step 29 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.00) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 13 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.62) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.20) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.75) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.39) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.05) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.97) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.69) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.50) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.01) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.35) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.37) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.02) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.86) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.29) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.15) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.88) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.86) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.46) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.87) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.79) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 14 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.21) 96% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.90) 92% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.50) 88% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent drove left instead of forward. (rewarded 1.20) 84% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.83) 80% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.34) 76% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.37) 72% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.16) 68% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.37) 64% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.22) 60% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.31) 56% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.65) 52% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 0.83) 48% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.99) 44% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.68) 40% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.40) 36% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded -0.28) 32% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.97) 28% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.10) 24% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 2.24) 20% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.34) 16% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.79) 12% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.18) 8% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.04) 4% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.17) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 15 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.33) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.70) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 2.03) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.24) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.29) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.44) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.29) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.81) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.46) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.49) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.42) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.19) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.57) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.24) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.98) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.78) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.28) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.77) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.91) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.81) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 16 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.74) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.79) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.29) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.62) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.42) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.09) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.19) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded -0.07) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 1.63) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 0.86) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.96) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.57) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.63) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.39) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.20) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.25) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.78) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.81) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.95) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded -0.80) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 17 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.02) 97% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.25) 93% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.33) 90% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.91) 87% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.48) 83% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.75) 80% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.44) 77% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.01) 73% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded -0.02) 70% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.85) 67% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.44) 63% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.36) 60% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.14) 57% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.37) 53% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.56) 50% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.67) 47% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.07) 43% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.29) 40% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.03) 37% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.71) 33% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.94) 30% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.08) 27% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.78) 23% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.99) 20% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.00) 17% of time remaining to reach destination. /------------------- | Step 25 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded -0.42) 13% of time remaining to reach destination. /------------------- | Step 26 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.44) 10% of time remaining to reach destination. /------------------- | Step 27 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.44) 7% of time remaining to reach destination. /------------------- | Step 28 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.94) 3% of time remaining to reach destination. /------------------- | Step 29 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.92) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 18 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 2.91) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.04) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.80) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.51) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.56) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.78) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.11) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.29) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.82) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.07) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.53) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.39) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.93) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.08) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 1.55) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.85) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.03) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.65) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.40) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.04) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 19 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.28) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.55) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.20) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.37) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.38) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 2.59) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.46) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.87) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.08) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.87) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.75) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.26) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.03) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.90) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.95) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.34) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.83) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.65) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.85) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.71) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Training trial 20 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.51) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.89) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.17) 85% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.17) 85% of time remaining to reach destination. !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.17) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.11) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded -0.05) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.76) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.05) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.39) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.49) 55% of time remaining to reach destination. Trial Completed! Agent reached the destination. /------------------------- | Testing trial 1 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 1.15) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.87) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.92) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.10) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.23) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.96) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.20) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.44) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.90) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.82) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.31) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.92) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.12) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 2.14) 30% of time remaining to reach destination. Trial Completed! Agent reached the destination. /------------------------- | Testing trial 2 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.05) 97% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.66) 93% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.14) 90% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.89) 87% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.73) 83% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.30) 80% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.25) 77% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.75) 73% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.16) 70% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.36) 67% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.08) 63% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.56) 60% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.36) 57% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.72) 53% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.36) 50% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.78) 47% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.16) 43% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.13) 40% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.94) 37% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.24) 33% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.81) 30% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.79) 27% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.49) 23% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.91) 20% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent drove left instead of forward. (rewarded 0.28) 17% of time remaining to reach destination. /------------------- | Step 25 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.29) 13% of time remaining to reach destination. /------------------- | Step 26 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.57) 10% of time remaining to reach destination. /------------------- | Step 27 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.44) 7% of time remaining to reach destination. /------------------- | Step 28 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 0.17) 3% of time remaining to reach destination. /------------------- | Step 29 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.93) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Testing trial 3 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.49) 97% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.79) 93% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.32) 90% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.51) 87% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.91) 83% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.57) 80% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.91) 77% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.52) 73% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.20) 70% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.64) 67% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.25) 63% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.56) 60% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.62) 57% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.40) 53% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.22) 50% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.62) 47% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.24) 43% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded -0.14) 40% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.27) 37% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.72) 33% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.99) 30% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded -0.04) 27% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.92) 23% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.39) 20% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 0.58) 17% of time remaining to reach destination. /------------------- | Step 25 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.21) 13% of time remaining to reach destination. /------------------- | Step 26 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.06) 10% of time remaining to reach destination. /------------------- | Step 27 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.73) 7% of time remaining to reach destination. /------------------- | Step 28 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.02) 3% of time remaining to reach destination. /------------------- | Step 29 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.97) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Testing trial 4 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.48) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.17) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.30) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.07) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.67) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.45) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.85) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.22) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.94) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.18) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.73) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.02) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.58) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.13) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.38) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.67) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.05) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.20) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.87) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.57) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Testing trial 5 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.32) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.86) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.16) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.76) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.83) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.41) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.20) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.92) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.46) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.65) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.17) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.71) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.15) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded -0.26) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.82) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded -0.30) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.09) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.07) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 0.64) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.66) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Testing trial 6 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.74) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.41) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.02) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.85) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.01) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.49) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -9.63) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.24) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.95) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.50) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 1.08) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.53) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.94) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 1.46) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.56) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.61) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.09) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.98) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 0.65) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.67) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Testing trial 7 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.16) 96% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.69) 92% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.39) 88% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.91) 84% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.78) 80% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.70) 76% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.45) 72% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.31) 68% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.51) 64% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.67) 60% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.25) 56% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.09) 52% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.23) 48% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.02) 44% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.94) 40% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 0.86) 36% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.27) 32% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent drove left instead of forward. (rewarded 1.16) 28% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 1.26) 24% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 0.01) 20% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.18) 16% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 1.10) 12% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 1.07) 8% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.59) 4% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.41) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Testing trial 8 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . 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Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.95) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.24) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.00) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 1.56) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 1.41) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.93) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.88) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.27) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.54) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.60) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -9.73) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 1.36) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.81) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 0.66) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.38) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.15) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 0.62) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.75) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.66) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.59) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Testing trial 9 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent drove left instead of right. (rewarded 0.27) 95% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.53) 90% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.78) 85% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 2.27) 80% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.53) 75% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent idled at a green light with oncoming traffic. (rewarded 0.94) 70% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent drove left instead of forward. (rewarded 1.50) 65% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent drove forward instead of right. (rewarded 1.20) 60% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.64) 55% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent followed the waypoint right. (rewarded 1.24) 50% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.72) 45% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded 1.05) 40% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -4.33) 35% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 1.95) 30% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light. (rewarded -10.22) 25% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 0.77) 20% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 0.44) 15% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.20) 10% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.56) 5% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.09) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. /------------------------- | Testing trial 10 \------------------------- Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. Simulating trial. . . Agent not set to learn. /------------------- | Step 0 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.07) 96% of time remaining to reach destination. /------------------- | Step 1 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 1.28) 92% of time remaining to reach destination. /------------------- | Step 2 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.73) 88% of time remaining to reach destination. /------------------- | Step 3 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.92) 84% of time remaining to reach destination. /------------------- | Step 4 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 1.26) 80% of time remaining to reach destination. /------------------- | Step 5 Results \------------------- !! Agent state not been updated! Agent followed the waypoint forward. (rewarded 2.09) 76% of time remaining to reach destination. /------------------- | Step 6 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded 1.04) 72% of time remaining to reach destination. /------------------- | Step 7 Results \------------------- !! Agent state not been updated! Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.64) 68% of time remaining to reach destination. /------------------- | Step 8 Results \------------------- !! Agent state not been updated! Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.41) 64% of time remaining to reach destination. /------------------- | Step 9 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.39) 60% of time remaining to reach destination. /------------------- | Step 10 Results \------------------- !! Agent state not been updated! Agent idled at a green light with no oncoming traffic. (rewarded -5.73) 56% of time remaining to reach destination. /------------------- | Step 11 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.99) 52% of time remaining to reach destination. /------------------- | Step 12 Results \------------------- !! Agent state not been updated! Agent drove right instead of forward. (rewarded -0.14) 48% of time remaining to reach destination. /------------------- | Step 13 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.08) 44% of time remaining to reach destination. /------------------- | Step 14 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.22) 40% of time remaining to reach destination. /------------------- | Step 15 Results \------------------- !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! 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(rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. !! Agent state not been updated! Agent properly idled at a red light. (rewarded 2.52) 36% of time remaining to reach destination. /------------------- | Step 16 Results \------------------- !! Agent state not been updated! Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.92) 32% of time remaining to reach destination. /------------------- | Step 17 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 0.91) 28% of time remaining to reach destination. /------------------- | Step 18 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.65) 24% of time remaining to reach destination. /------------------- | Step 19 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.23) 20% of time remaining to reach destination. /------------------- | Step 20 Results \------------------- !! Agent state not been updated! Agent attempted driving forward through a red light. (rewarded -10.16) 16% of time remaining to reach destination. /------------------- | Step 21 Results \------------------- !! Agent state not been updated! Agent drove right instead of left. (rewarded -0.41) 12% of time remaining to reach destination. /------------------- | Step 22 Results \------------------- !! Agent state not been updated! Agent drove forward instead of left. (rewarded 1.12) 8% of time remaining to reach destination. /------------------- | Step 23 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 0.28) 4% of time remaining to reach destination. /------------------- | Step 24 Results \------------------- !! Agent state not been updated! Agent followed the waypoint left. (rewarded 0.98) 0% of time remaining to reach destination. Trial Aborted! Agent did not reach the destination. Simulation ended. . .
In [3]:
# Load the 'sim_no-learning' log file from the initial simulation results
vs.plot_trials('sim_no-learning.csv')
Question 3¶
Using the visualization above that was produced from your initial simulation, provide an analysis and make several observations about the driving agent. Be sure that you are making at least one observation about each panel present in the visualization. Some things you could consider:
- How frequently is the driving agent making bad decisions? How many of those bad decisions cause accidents?
- Given that the agent is driving randomly, does the rate of reliabilty make sense?
- What kind of rewards is the agent receiving for its actions? Do the rewards suggest it has been penalized heavily?
- As the number of trials increases, does the outcome of results change significantly?
- Would this Smartcab be considered safe and/or reliable for its passengers? Why or why not?
Answer:
The agent is making bad actions about 35% to 40% of the time in each trial. About 5% to 7% of the time, this leads to major accidents.
More often than not, the smart cab won't reach its destination since it is simply driving around randomly. The poor rate of reliability here is due to the agent simply exploring but not learning. So it makes sense to have the rate of reliability here from this point of view. We can deduce that randomly driving around is not a reliable method of reaching one's destination.
The agent is receiving a value. In our simulation above, it is receiving negative rewards on average. It is receiving around -5 per action on average which suggests heavy penalty but we have not enabled learning yet.
Since we have not enabled the agent to learn, the outcome should not change in this case. The rate of reliability also remains constant at around 10%.
This smarcab would definitely not be considered safe or reliable becuase of the high rate of accidents both major and minor and it does not reach the destination as well most of the time.
Inform the Driving Agent¶
The second step to creating an optimized Q-learning driving agent is defining a set of states that the agent can occupy in the environment. Depending on the input, sensory data, and additional variables available to the driving agent, a set of states can be defined for the agent so that it can eventually learn what action it should take when occupying a state. The condition of 'if state then action' for each state is called a policy, and is ultimately what the driving agent is expected to learn. Without defining states, the driving agent would never understand which action is most optimal -- or even what environmental variables and conditions it cares about!
Identify States¶
Inspecting the 'build_state()' agent function shows that the driving agent is given the following data from the environment:
'waypoint', which is the direction the Smartcab should drive leading to the destination, relative to the Smartcab's heading.'inputs', which is the sensor data from the Smartcab. It includes'light', the color of the light.'left', the intended direction of travel for a vehicle to the Smartcab's left. ReturnsNoneif no vehicle is present.'right', the intended direction of travel for a vehicle to the Smartcab's right. ReturnsNoneif no vehicle is present.'oncoming', the intended direction of travel for a vehicle across the intersection from the Smartcab. ReturnsNoneif no vehicle is present.
'deadline', which is the number of actions remaining for the Smartcab to reach the destination before running out of time.
Question 4¶
Which features available to the agent are most relevant for learning both safety and efficiency? Why are these features appropriate for modeling the Smartcab in the environment? If you did not choose some features, why are those features not appropriate?
Answer:
Waypoint is a feature relevant for efficiency. It allows the agent to plan its route based on location and heading. Without it we will not be able to direct the agent to our destination. Hence, this is important.
Inputs is feature that is relevant for safety. Without it the agent would not be able to avoid other agents on the road. Hence, this is an important feature.
Deadline thought relevant to improve efficency, it isn't the most relevant for safety as it could possibly invoke a bad action from an agent such as moving during a red light.
Define a State Space¶
When defining a set of states that the agent can occupy, it is necessary to consider the size of the state space. That is to say, if you expect the driving agent to learn a policy for each state, you would need to have an optimal action for every state the agent can occupy. If the number of all possible states is very large, it might be the case that the driving agent never learns what to do in some states, which can lead to uninformed decisions. For example, consider a case where the following features are used to define the state of the Smartcab:
('is_raining', 'is_foggy', 'is_red_light', 'turn_left', 'no_traffic', 'previous_turn_left', 'time_of_day').
How frequently would the agent occupy a state like (False, True, True, True, False, False, '3AM')? Without a near-infinite amount of time for training, it's doubtful the agent would ever learn the proper action!
Question 5¶
If a state is defined using the features you've selected from Question 4, what would be the size of the state space? Given what you know about the evironment and how it is simulated, do you think the driving agent could learn a policy for each possible state within a reasonable number of training trials?
Hint: Consider the combinations of features to calculate the total number of states!
Answer:
Waypoint has 3 features of "forward, "left" and "right". Inputs have 4 features of "light", "left", "right", "oncoming". Of which, light has 2 features and left, right and oncoming have 4 features. So the total number of states is 3 x 2 x 4 x 4 x 4 = 384 features. The driving agent could not learn a policy for each possible state within a reasonable number of training trials.
We could implement with Waypoint and 3 of the 4 features of Inputs, namely, light, oncoming and left. This would take the total number of states to 3 x 2 x 4 x 4=96 which is feasible for the driving agent to learn a policy within a reasonable number of training trials. Also, it captures the most relevant features of not running a red light and to look out for oncoming vehicles. While, it is green light, our agent would have the right of way. At a red light it is possible for the agent to turn right given the there is no oncoming from the left. These set of state space would consider whether the oncoming vehicle would be turning into its path or into the agent.
We can see how many trials would be needed to for the agent to learn the outcomes of these 96 state spaces by running a monte carlo simulation.
In [2]:
from sets import Set
from random import choice
def chance_of_visiting_all_states(iterations, k, n=24):
r = range(n)
total = 0
for i in range(iterations):
s = Set()
for j in range(k):
s.add(choice(r))
if len(s) == n:
total +=1
break
return float(total)/iterations
steps = 750
print "Chance of visiting all states in {st} \
steps: {ch}".format(st = steps, ch = chance_of_visiting_all_states(2000, steps, 96))
C:\Users\User\Anaconda2\lib\site-packages\ipykernel\__main__.py:1: DeprecationWarning: the sets module is deprecated if __name__ == '__main__':
Chance of visiting all states in 750 steps: 0.9575
In [4]:
steps2 = 1200
print "Chance of visiting all states in {st} \
steps: {ch}".format(st = steps2 , ch = chance_of_visiting_all_states(4000, steps2, 96))
Chance of visiting all states in 1200 steps: 0.99925
Update the Driving Agent State¶
For your second implementation, navigate to the 'build_state()' agent function. With the justification you've provided in Question 4, you will now set the 'state' variable to a tuple of all the features necessary for Q-Learning. Confirm your driving agent is updating its state by running the agent file and simulation briefly and note whether the state is displaying. If the visual simulation is used, confirm that the updated state corresponds with what is seen in the simulation.
Note: Remember to reset simulation flags to their default setting when making this observation!
In [4]:
!python smartcab/agent.py
/-------------------------
| Training trial 1
\-------------------------
Simulating trial. . .
Agent not set to learn.
/-------------------
| Step 0 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None})
Agent attempted driving left through a red light. (rewarded -10.17)
Agent not enforced to meet deadline.
/-------------------
| Step 1 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None})
Agent attempted driving left through a red light. (rewarded -10.00)
Agent not enforced to meet deadline.
/-------------------
| Step 2 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'})
Agent properly idled at a red light. (rewarded 2.19)
Agent not enforced to meet deadline.
/-------------------
| Step 3 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None})
Agent attempted driving left through a red light. (rewarded -9.63)
Agent not enforced to meet deadline.
/-------------------
| Step 4 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None})
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.85)
Agent not enforced to meet deadline.
/-------------------
| Step 5 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'})
Agent attempted driving left through a red light. (rewarded -10.34)
Agent not enforced to meet deadline.
/-------------------
| Step 6 Results
\-------------------
Agent previous state: ('right', {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None})
Agent idled at a green light with oncoming traffic. (rewarded 0.01)
Agent not enforced to meet deadline.
/-------------------
| Step 7 Results
\-------------------
Agent previous state: ('right', {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'})
Agent drove forward instead of right. (rewarded 1.00)
Agent not enforced to meet deadline.
/-------------------
| Step 8 Results
\-------------------
Agent previous state: ('right', {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None})
Agent idled at a green light with no oncoming traffic. (rewarded -4.92)
Agent not enforced to meet deadline.
/-------------------
| Step 9 Results
\-------------------
Agent previous state: ('right', {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None})
Agent idled at a green light with no oncoming traffic. (rewarded -5.13)
Agent not enforced to meet deadline.
/-------------------
| Step 10 Results
\-------------------
Agent previous state: ('right', {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None})
Agent idled at a green light with no oncoming traffic. (rewarded -4.98)
Agent not enforced to meet deadline.
/-------------------
| Step 11 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None})
Agent attempted driving left through a red light. (rewarded -9.89)
Agent not enforced to meet deadline.
/-------------------
| Step 12 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None})
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.01)
Agent not enforced to meet deadline.
/-------------------
| Step 13 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None})
Agent properly idled at a red light. (rewarded 2.90)
Agent not enforced to meet deadline.
/-------------------
| Step 14 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None})
Agent attempted driving left through a red light. (rewarded -9.74)
Agent not enforced to meet deadline.
/-------------------
| Step 15 Results
\-------------------
Agent previous state: ('right', {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'})
Agent followed the waypoint right. (rewarded 2.52)
Agent not enforced to meet deadline.
/-------------------
| Step 16 Results
\-------------------
Agent previous state: ('forward', {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'})
Agent properly idled at a red light. (rewarded 1.18)
Agent not enforced to meet deadline.
/-------------------
| Step 17 Results
\-------------------
Agent previous state: ('forward', {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None})
Agent drove right instead of forward. (rewarded 1.79)
Agent not enforced to meet deadline.
/-------------------
| Step 18 Results
\-------------------
Agent previous state: ('left', {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None})
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.70)
Agent not enforced to meet deadline.
/-------------------
| Step 19 Results
\-------------------
Agent previous state: ('left', {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'})
Agent drove right instead of left. (rewarded 0.91)
Agent not enforced to meet deadline.
/-------------------
| Step 20 Results
\-------------------
Agent previous state: ('left', {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None})
Agent drove right instead of left. (rewarded 0.58)
Agent not enforced to meet deadline.
Simulation ended. . .
Implement a Q-Learning Driving Agent¶
The third step to creating an optimized Q-Learning agent is to begin implementing the functionality of Q-Learning itself. The concept of Q-Learning is fairly straightforward: For every state the agent visits, create an entry in the Q-table for all state-action pairs available. Then, when the agent encounters a state and performs an action, update the Q-value associated with that state-action pair based on the reward received and the interative update rule implemented. Of course, additional benefits come from Q-Learning, such that we can have the agent choose the best action for each state based on the Q-values of each state-action pair possible. For this project, you will be implementing a decaying, $\epsilon$-greedy Q-learning algorithm with no discount factor. Follow the implementation instructions under each TODO in the agent functions.
Note that the agent attribute self.Q is a dictionary: This is how the Q-table will be formed. Each state will be a key of the self.Q dictionary, and each value will then be another dictionary that holds the action and Q-value. Here is an example:
{ 'state-1': {
'action-1' : Qvalue-1,
'action-2' : Qvalue-2,
...
},
'state-2': {
'action-1' : Qvalue-1,
...
},
...
}Furthermore, note that you are expected to use a decaying $\epsilon$ (exploration) factor. Hence, as the number of trials increases, $\epsilon$ should decrease towards 0. This is because the agent is expected to learn from its behavior and begin acting on its learned behavior. Additionally, The agent will be tested on what it has learned after $\epsilon$ has passed a certain threshold (the default threshold is 0.01). For the initial Q-Learning implementation, you will be implementing a linear decaying function for $\epsilon$.
In [17]:
!python smartcab/agent.py
/-------------------------
| Training trial 1
\-------------------------
Simulating trial. . .
Agent not set to learn.
/-------------------
| Step 0 Results
\-------------------
('right', 'green', 'left', 'left', 'left')
Agent previous state: ('right', 'green', 'left', 'left', 'left')
Agent drove forward instead of right. (rewarded 1.17)
Agent not enforced to meet deadline.
/-------------------
| Step 1 Results
\-------------------
('right', 'red', 'forward', None, None)
Agent previous state: ('right', 'red', 'forward', None, None)
Agent attempted driving left through a red light. (rewarded -10.46)
Agent not enforced to meet deadline.
/-------------------
| Step 2 Results
\-------------------
('right', 'red', 'forward', None, None)
Agent previous state: ('right', 'red', 'forward', None, None)
Agent followed the waypoint right. (rewarded 1.95)
Agent not enforced to meet deadline.
/-------------------
| Step 3 Results
\-------------------
('forward', 'red', None, 'right', None)
Agent previous state: ('forward', 'red', None, 'right', None)
Agent attempted driving left through a red light. (rewarded -9.25)
Agent not enforced to meet deadline.
/-------------------
| Step 4 Results
\-------------------
('forward', 'red', 'right', None, None)
Agent previous state: ('forward', 'red', 'right', None, None)
Agent properly idled at a red light. (rewarded 2.44)
Agent not enforced to meet deadline.
/-------------------
| Step 5 Results
\-------------------
('forward', 'red', None, None, None)
Agent previous state: ('forward', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -10.84)
Agent not enforced to meet deadline.
/-------------------
| Step 6 Results
\-------------------
('forward', 'green', None, None, 'forward')
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent followed the waypoint forward. (rewarded 1.77)
Agent not enforced to meet deadline.
/-------------------
| Step 7 Results
\-------------------
('left', 'green', 'right', None, None)
Agent previous state: ('left', 'green', 'right', None, None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.58)
Agent not enforced to meet deadline.
/-------------------
| Step 8 Results
\-------------------
('left', 'green', None, 'left', None)
Agent previous state: ('left', 'green', None, 'left', None)
Agent drove right instead of left. (rewarded 0.75)
Agent not enforced to meet deadline.
/-------------------
| Step 9 Results
\-------------------
('forward', 'red', 'left', None, None)
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded 0.93)
Agent not enforced to meet deadline.
/-------------------
| Step 10 Results
\-------------------
('left', 'green', None, 'left', None)
Agent previous state: ('left', 'green', None, 'left', None)
Agent drove right instead of left. (rewarded 1.24)
Agent not enforced to meet deadline.
/-------------------
| Step 11 Results
\-------------------
('right', 'green', 'left', 'forward', 'left')
Agent previous state: ('right', 'green', 'left', 'forward', 'left')
Agent drove left instead of right. (rewarded 1.65)
Agent not enforced to meet deadline.
/-------------------
| Step 12 Results
\-------------------
('right', 'red', None, 'right', None)
Agent previous state: ('right', 'red', None, 'right', None)
Agent attempted driving forward through a red light. (rewarded -11.00)
Agent not enforced to meet deadline.
/-------------------
| Step 13 Results
\-------------------
('right', 'green', 'forward', None, None)
Agent previous state: ('right', 'green', 'forward', None, None)
Agent followed the waypoint right. (rewarded 2.56)
Agent not enforced to meet deadline.
/-------------------
| Step 14 Results
\-------------------
('right', 'green', None, None, None)
Agent previous state: ('right', 'green', None, None, None)
Agent drove forward instead of right. (rewarded 1.02)
Agent not enforced to meet deadline.
/-------------------
| Step 15 Results
\-------------------
('right', 'red', 'forward', 'left', None)
Agent previous state: ('right', 'red', 'forward', 'left', None)
Agent followed the waypoint right. (rewarded 1.88)
Agent not enforced to meet deadline.
/-------------------
| Step 16 Results
\-------------------
('forward', 'green', None, 'left', 'forward')
Agent previous state: ('forward', 'green', None, 'left', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.92)
Agent not enforced to meet deadline.
/-------------------
| Step 17 Results
\-------------------
('forward', 'red', None, 'left', 'forward')
Agent previous state: ('forward', 'red', None, 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.35)
Agent not enforced to meet deadline.
/-------------------
| Step 18 Results
\-------------------
('forward', 'red', None, None, None)
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.61)
Agent not enforced to meet deadline.
/-------------------
| Step 19 Results
\-------------------
('forward', 'red', None, None, None)
Agent previous state: ('forward', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -10.93)
Agent not enforced to meet deadline.
/-------------------
| Step 20 Results
\-------------------
('forward', 'red', None, None, None)
Agent previous state: ('forward', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -10.54)
Agent not enforced to meet deadline.
/-------------------
| Step 21 Results
\-------------------
('forward', 'green', None, 'right', None)
Agent previous state: ('forward', 'green', None, 'right', None)
Agent drove left instead of forward. (rewarded 0.18)
Agent not enforced to meet deadline.
/-------------------
| Step 22 Results
\-------------------
('right', 'red', None, 'forward', 'forward')
Agent previous state: ('right', 'red', None, 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.21)
Agent not enforced to meet deadline.
/-------------------
| Step 23 Results
\-------------------
('right', 'red', None, None, None)
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 2.85)
Agent not enforced to meet deadline.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 2
\-------------------------
Simulating trial. . .
Agent not set to learn.
/-------------------
| Step 0 Results
\-------------------
('forward', 'green', None, None, None)
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 2.24)
Agent not enforced to meet deadline.
/-------------------
| Step 1 Results
\-------------------
('forward', 'green', 'forward', 'forward', 'right')
Agent previous state: ('forward', 'green', 'forward', 'forward', 'right')
Agent followed the waypoint forward. (rewarded 1.98)
Agent not enforced to meet deadline.
/-------------------
| Step 2 Results
\-------------------
('forward', 'red', 'left', None, None)
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded 1.80)
Agent not enforced to meet deadline.
/-------------------
| Step 3 Results
\-------------------
('left', 'green', None, None, 'left')
Agent previous state: ('left', 'green', None, None, 'left')
Agent drove forward instead of left. (rewarded 1.40)
Agent not enforced to meet deadline.
/-------------------
| Step 4 Results
\-------------------
('left', 'green', 'left', None, None)
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove right instead of left. (rewarded 1.81)
Agent not enforced to meet deadline.
/-------------------
| Step 5 Results
\-------------------
('left', 'green', 'left', None, None)
Agent previous state: ('left', 'green', 'left', None, None)
Agent followed the waypoint left. (rewarded 1.79)
Agent not enforced to meet deadline.
/-------------------
| Step 6 Results
\-------------------
('left', 'red', 'left', None, 'left')
Agent previous state: ('left', 'red', 'left', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.24)
Agent not enforced to meet deadline.
/-------------------
| Step 7 Results
\-------------------
('left', 'red', 'left', 'forward', 'left')
Agent previous state: ('left', 'red', 'left', 'forward', 'left')
Agent drove right instead of left. (rewarded 1.09)
Agent not enforced to meet deadline.
/-------------------
| Step 8 Results
\-------------------
('left', 'green', None, 'forward', None)
Agent previous state: ('left', 'green', None, 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.35)
Agent not enforced to meet deadline.
/-------------------
| Step 9 Results
\-------------------
('left', 'red', None, 'forward', None)
Agent previous state: ('left', 'red', None, 'forward', None)
Agent properly idled at a red light. (rewarded 1.78)
Agent not enforced to meet deadline.
/-------------------
| Step 10 Results
\-------------------
('left', 'red', None, 'forward', None)
Agent previous state: ('left', 'red', None, 'forward', None)
Agent properly idled at a red light. (rewarded 2.61)
Agent not enforced to meet deadline.
/-------------------
| Step 11 Results
\-------------------
('left', 'green', None, None, None)
Agent previous state: ('left', 'green', None, None, None)
Agent drove forward instead of left. (rewarded 0.68)
Agent not enforced to meet deadline.
/-------------------
| Step 12 Results
\-------------------
('forward', 'green', None, 'left', None)
Agent previous state: ('forward', 'green', None, 'left', None)
Agent followed the waypoint forward. (rewarded 2.99)
Agent not enforced to meet deadline.
/-------------------
| Step 13 Results
\-------------------
('forward', 'green', 'left', None, None)
Agent previous state: ('forward', 'green', 'left', None, None)
Agent drove right instead of forward. (rewarded 0.87)
Agent not enforced to meet deadline.
/-------------------
| Step 14 Results
\-------------------
('left', 'red', 'left', None, None)
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 0.20)
Agent not enforced to meet deadline.
/-------------------
| Step 15 Results
\-------------------
('right', 'green', None, None, 'forward')
Agent previous state: ('right', 'green', None, None, 'forward')
Agent drove forward instead of right. (rewarded 0.71)
Agent not enforced to meet deadline.
/-------------------
| Step 16 Results
\-------------------
('forward', 'red', None, None, None)
Agent previous state: ('forward', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -10.35)
Agent not enforced to meet deadline.
/-------------------
| Step 17 Results
\-------------------
('forward', 'green', None, None, None)
Agent previous state: ('forward', 'green', None, None, None)
Agent drove left instead of forward. (rewarded 0.13)
Agent not enforced to meet deadline.
/-------------------
| Step 18 Results
\-------------------
('right', 'red', None, None, 'forward')
Agent previous state: ('right', 'red', None, None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.53)
Agent not enforced to meet deadline.
/-------------------
| Step 19 Results
\-------------------
('right', 'red', None, None, None)
Agent previous state: ('right', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -9.96)
Agent not enforced to meet deadline.
/-------------------
| Step 20 Results
\-------------------
('right', 'red', None, None, None)
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 2.64)
Agent not enforced to meet deadline.
/-------------------
| Step 21 Results
\-------------------
('forward', 'red', 'left', 'forward', 'forward')
Agent previous state: ('forward', 'red', 'left', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.27)
Agent not enforced to meet deadline.
/-------------------
| Step 22 Results
\-------------------
('forward', 'red', 'left', None, 'left')
Agent previous state: ('forward', 'red', 'left', None, 'left')
Agent properly idled at a red light. (rewarded 2.15)
Agent not enforced to meet deadline.
/-------------------
| Step 23 Results
\-------------------
('forward', 'red', 'left', None, None)
Agent previous state: ('forward', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -9.08)
Agent not enforced to meet deadline.
/-------------------
| Step 24 Results
\-------------------
('forward', 'red', 'left', 'forward', None)
Agent previous state: ('forward', 'red', 'left', 'forward', None)
Agent properly idled at a red light. (rewarded 2.81)
Agent not enforced to meet deadline.
/-------------------
| Step 25 Results
\-------------------
('forward', 'green', 'left', None, None)
Agent previous state: ('forward', 'green', 'left', None, None)
Agent idled at a green light with oncoming traffic. (rewarded 0.25)
Agent not enforced to meet deadline.
/-------------------
| Step 26 Results
\-------------------
('forward', 'green', 'right', 'left', None)
Agent previous state: ('forward', 'green', 'right', 'left', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.28)
Agent not enforced to meet deadline.
/-------------------
| Step 27 Results
\-------------------
('forward', 'green', None, 'left', None)
Agent previous state: ('forward', 'green', None, 'left', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.02)
Agent not enforced to meet deadline.
/-------------------
| Step 28 Results
\-------------------
('forward', 'green', 'forward', 'left', None)
Agent previous state: ('forward', 'green', 'forward', 'left', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.35)
Agent not enforced to meet deadline.
/-------------------
| Step 29 Results
\-------------------
('forward', 'red', None, 'forward', None)
Agent previous state: ('forward', 'red', None, 'forward', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.41)
Agent not enforced to meet deadline.
/-------------------
| Step 30 Results
\-------------------
('forward', 'red', None, None, None)
Agent previous state: ('forward', 'red', None, None, None)
Agent drove right instead of forward. (rewarded 1.30)
Agent not enforced to meet deadline.
/-------------------
| Step 31 Results
\-------------------
('left', 'red', 'forward', None, None)
Agent previous state: ('left', 'red', 'forward', None, None)
Agent attempted driving forward through a red light. (rewarded -10.68)
Agent not enforced to meet deadline.
/-------------------
| Step 32 Results
\-------------------
('left', 'red', 'forward', 'forward', None)
Agent previous state: ('left', 'red', 'forward', 'forward', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.44)
Agent not enforced to meet deadline.
/-------------------
| Step 33 Results
\-------------------
('left', 'green', 'forward', None, None)
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove forward instead of left. (rewarded 0.63)
Agent not enforced to meet deadline.
/-------------------
| Step 34 Results
\-------------------
('left', 'green', 'right', None, 'forward')
Agent previous state: ('left', 'green', 'right', None, 'forward')
Agent drove right instead of left. (rewarded 0.06)
Agent not enforced to meet deadline.
/-------------------
| Step 35 Results
\-------------------
('left', 'green', None, None, None)
Agent previous state: ('left', 'green', None, None, None)
Agent drove right instead of left. (rewarded 0.24)
Agent not enforced to meet deadline.
/-------------------
| Step 36 Results
\-------------------
('right', 'green', 'right', 'left', 'forward')
Agent previous state: ('right', 'green', 'right', 'left', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.21)
Agent not enforced to meet deadline.
/-------------------
| Step 37 Results
\-------------------
('right', 'red', None, 'left', 'forward')
Agent previous state: ('right', 'red', None, 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.42)
Agent not enforced to meet deadline.
/-------------------
| Step 38 Results
\-------------------
('right', 'red', 'right', 'left', 'forward')
Agent previous state: ('right', 'red', 'right', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.75)
Agent not enforced to meet deadline.
/-------------------
| Step 39 Results
\-------------------
('right', 'green', 'forward', None, None)
Agent previous state: ('right', 'green', 'forward', None, None)
Agent followed the waypoint right. (rewarded 2.21)
Agent not enforced to meet deadline.
/-------------------
| Step 40 Results
\-------------------
('forward', 'green', None, None, None)
Agent previous state: ('forward', 'green', None, None, None)
Agent drove left instead of forward. (rewarded 0.26)
Agent not enforced to meet deadline.
/-------------------
| Step 41 Results
\-------------------
('right', 'red', None, 'left', 'forward')
Agent previous state: ('right', 'red', None, 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.80)
Agent not enforced to meet deadline.
/-------------------
| Step 42 Results
\-------------------
('right', 'red', None, None, 'left')
Agent previous state: ('right', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 2.07)
Agent not enforced to meet deadline.
/-------------------
| Step 43 Results
\-------------------
('right', 'red', None, 'right', None)
Agent previous state: ('right', 'red', None, 'right', None)
Agent followed the waypoint right. (rewarded 2.21)
Agent not enforced to meet deadline.
/-------------------
| Step 44 Results
\-------------------
('forward', 'red', 'forward', None, None)
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent drove right instead of forward. (rewarded 1.15)
Agent not enforced to meet deadline.
/-------------------
| Step 45 Results
\-------------------
('left', 'red', 'left', None, None)
Agent previous state: ('left', 'red', 'left', None, None)
Agent attempted driving forward through a red light. (rewarded -10.42)
Agent not enforced to meet deadline.
/-------------------
| Step 46 Results
\-------------------
('left', 'green', 'left', 'forward', 'forward')
Agent previous state: ('left', 'green', 'left', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 0.06)
Agent not enforced to meet deadline.
/-------------------
| Step 47 Results
\-------------------
('left', 'red', None, 'left', None)
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 2.38)
Agent not enforced to meet deadline.
/-------------------
| Step 48 Results
\-------------------
('left', 'red', None, 'right', 'left')
Agent previous state: ('left', 'red', None, 'right', 'left')
Agent attempted driving forward through a red light. (rewarded -9.86)
Agent not enforced to meet deadline.
/-------------------
| Step 49 Results
\-------------------
('left', 'red', None, None, 'left')
Agent previous state: ('left', 'red', None, None, 'left')
Agent drove right instead of left. (rewarded 0.11)
Agent not enforced to meet deadline.
/-------------------
| Step 50 Results
\-------------------
('left', 'green', 'right', 'forward', None)
Agent previous state: ('left', 'green', 'right', 'forward', None)
Agent drove forward instead of left. (rewarded 0.71)
Agent not enforced to meet deadline.
/-------------------
| Step 51 Results
\-------------------
('left', 'green', None, None, None)
Agent previous state: ('left', 'green', None, None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.11)
Agent not enforced to meet deadline.
/-------------------
| Step 52 Results
\-------------------
('left', 'green', None, None, 'right')
Agent previous state: ('left', 'green', None, None, 'right')
Agent drove forward instead of left. (rewarded 1.99)
Agent not enforced to meet deadline.
/-------------------
| Step 53 Results
\-------------------
('forward', 'red', None, None, 'left')
Agent previous state: ('forward', 'red', None, None, 'left')
Agent attempted driving left through a red light. (rewarded -10.06)
Agent not enforced to meet deadline.
/-------------------
| Step 54 Results
\-------------------
('forward', 'red', None, None, None)
Agent previous state: ('forward', 'red', None, None, None)
Agent drove right instead of forward. (rewarded 0.77)
Agent not enforced to meet deadline.
Simulation ended. . .
Q-Learning Simulation Results¶
To obtain results from the initial Q-Learning implementation, you will need to adjust the following flags and setup:
'enforce_deadline'- Set this toTrueto force the driving agent to capture whether it reaches the destination in time.'update_delay'- Set this to a small value (such as0.01) to reduce the time between steps in each trial.'log_metrics'- Set this toTrueto log the simluation results as a.csvfile and the Q-table as a.txtfile in/logs/.'n_test'- Set this to'10'to perform 10 testing trials.'learning'- Set this to'True'to tell the driving agent to use your Q-Learning implementation.
In addition, use the following decay function for $\epsilon$:
$$ \epsilon_{t+1} = \epsilon_{t} - 0.05, \hspace{10px}\textrm{for trial number } t$$If you have difficulty getting your implementation to work, try setting the 'verbose' flag to True to help debug. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!
Once you have successfully completed the initial Q-Learning simulation, run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!
In [19]:
!python smartcab/agent.py
/-------------------------
| Training trial 1
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (2, 3), deadline = 25
0.95
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
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epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'left', 'forward')
New state created!
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: None, reward: -5.66164766547
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', 'left', 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': -5.661647665467211, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.66)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'left', 'forward')
New state created!
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: forward, reward: 0.323650668999
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', 'forward'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 0.3236506689991807, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', 'forward')
Agent drove forward instead of left. (rewarded 0.32)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, None, 'left')
New state created!
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: forward, reward: 0.536389908825
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'left'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 0.5363899088254529, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'left')
Agent drove forward instead of left. (rewarded 0.54)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None, 'right')
New state created!
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 1.03808141827
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, 'right'), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.0380814182655433, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, 'right')
Agent drove right instead of left. (rewarded 1.04)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None, 'forward')
New state created!
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: left, reward: 0.574971244403
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'forward'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 0.5749712444033886, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent drove left instead of forward. (rewarded 0.57)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'left', None, None)
New state created!
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: right, reward: 1.16138004625
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.1613800462463075, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.16)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None, None)
New state created!
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: forward, reward: 2.54649507628
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 2.5464950762835192, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None, None)
Agent followed the waypoint forward. (rewarded 2.55)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None, 'forward')
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: -4.33087660783
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': -4.330876607833735, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.33)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'right', 'forward')
New state created!
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: 2.32893441073
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right', 'forward'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 2.328934410729597, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right', 'forward')
Agent followed the waypoint forward. (rewarded 2.33)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None, 'right')
New state created!
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -10.6557632257
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, 'right'), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -10.655763225706103, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'right')
Agent attempted driving left through a red light. (rewarded -10.66)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None, None)
New state created!
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -10.4292727259
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -10.429272725888042, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -10.43)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'right', 'left', 'right')
New state created!
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 1.19099137561
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left', 'right'), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 1.190991375609035, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left', 'right')
Agent drove forward instead of left. (rewarded 1.19)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -9.03440203561
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -9.034402035609268, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -9.03)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'right', None, None)
New state created!
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: -5.97599476126
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': -5.975994761264729, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.98)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None, None)
New state created!
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: 0.514422120253
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.5144221202533769, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove right instead of left. (rewarded 0.51)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', None, None)
New state created!
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: forward, reward: -9.35763227205
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None, None), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': -9.357632272045564, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, None)
Agent attempted driving forward through a red light. (rewarded -9.36)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 1.07218318928
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 1.0721831892779994, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, None)
Agent followed the waypoint right. (rewarded 1.07)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'left', None)
New state created!
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 1.67228231067
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', None), 'deadline': 8, 't': 17, 'action': None, 'reward': 1.6722823106696663, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 1.67)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'right', 'forward')
New state created!
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 2.16427048906
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right', 'forward'), 'deadline': 7, 't': 18, 'action': None, 'reward': 2.1642704890583246, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right', 'forward')
Agent properly idled at a red light. (rewarded 2.16)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None, 'forward')
New state created!
0.0
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: 1.68547361179
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, 'forward'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.6854736117910825, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, 'forward')
Agent followed the waypoint right. (rewarded 1.69)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', 'left', 'forward', None)
New state created!
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: 1.15483331027
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward', None), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': 1.1548333102737043, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.15)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None, None)
New state created!
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: forward, reward: 2.02929146856
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 2.0292914685597703, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 2.03)
12% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 2
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (6, 4), deadline = 20
0.9
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'left', None)
New state created!
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: -9.83921839037
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'left', None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -9.839218390372025, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.84)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None, None)
New state created!
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.38187838786
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.3818783878614262, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent properly idled at a red light. (rewarded 1.38)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: forward, reward: -9.03956433022
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -9.039564330220493, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent attempted driving forward through a red light. (rewarded -9.04)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'forward', None, None)
New state created!
0.0
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 1.62254187256
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.622541872559483, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove right instead of left. (rewarded 1.62)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'left', None)
New state created!
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 2.37061676995
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.3706167699454044, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 2.37)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 0.919755789465
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 0.9197557894646915, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.92)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: -4.54077989293
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': -4.540779892931326, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.54)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None, 'forward')
New state created!
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 1.28634335064
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'forward'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.2863433506402846, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'forward')
Agent drove forward instead of left. (rewarded 1.29)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'left', 'left', 'right')
New state created!
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -0.0710223539376
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left', 'right'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -0.07102235393758038, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left', 'right')
Agent drove left instead of forward. (rewarded -0.07)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None, None)
New state created!
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -9.60662756101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -9.606627561011095, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -9.61)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None, None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 0.766340036348
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.766340036347716, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 0.77)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.49977532597
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward', 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.4997753259705078, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 1.50)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'forward', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 1.84764820735
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward', 'left'), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 1.847648207354523, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.85)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, 'left', 'forward')
New state created!
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 1.68434821506
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left', 'forward'), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.684348215062496, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.68)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None, None)
New state created!
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.25630674869
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.2563067486886972, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent drove right instead of forward. (rewarded 1.26)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'forward', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 0.277229163032
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', 'forward'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.27722916303168743, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', 'forward')
Agent drove right instead of left. (rewarded 0.28)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'left', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -9.95101677026
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -9.951016770263267, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -9.95)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'left', None, None)
New state created!
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 0.725184814742
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.725184814741646, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 0.73)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'left', 'forward')
New state created!
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: 0.46909453333
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', 'forward'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': 0.4690945333301617, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', 'forward')
Agent drove left instead of right. (rewarded 0.47)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, 'left', None)
New state created!
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: -4.50730864883
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': -4.5073086488319944, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'left', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.51)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 3
\-------------------------
Environment.reset(): Trial set up with start = (3, 6), destination = (1, 2), deadline = 20
0.85
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left', 'right')
New state created!
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 2.18919723346
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', 'right'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.1891972334552867, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', 'right')
Agent followed the waypoint right. (rewarded 2.19)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 2.1513297448
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.1513297447972866, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, None)
Agent followed the waypoint right. (rewarded 2.15)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None, None)
New state created!
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -10.2138488776
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.21384887757884, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -10.21)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', 'forward', 'forward')
New state created!
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: -20.20114223
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward', 'forward'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': -20.20114223004652, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.20)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: -9.06539695666
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -9.06539695666369, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent attempted driving forward through a red light. (rewarded -9.07)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 1.40231590501
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.4023159050096425, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded 1.40)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'right', None)
New state created!
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: -9.52325512189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -9.52325512188719, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.52)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: -4.21175640928
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': -4.2117564092787525, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.21)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: 0.144863311822
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 0.14486331182153733, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove forward instead of left. (rewarded 0.14)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None, None)
New state created!
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 1.00069303781
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.0006930378079009, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove right instead of left. (rewarded 1.00)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'right', None, 'right')
New state created!
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: None, reward: 2.45727632142
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None, 'right'), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.457276321422058, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None, 'right')
Agent properly idled at a red light. (rewarded 2.46)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: 2.28414026472
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.284140264717907, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 2.28)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, 'right', None)
New state created!
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 0.922257839532
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.9222578395316421, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right', None)
Agent properly idled at a red light. (rewarded 0.92)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: forward, reward: 1.38524239958
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.385242399581256, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent drove forward instead of right. (rewarded 1.39)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: forward, reward: -9.00098860829
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -9.000988608288566, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -9.00)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None, None)
New state created!
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 0.628067319132
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 0.6280673191321686, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 0.63)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'left', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: -10.5315736713
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -10.531573671316433, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent attempted driving forward through a red light. (rewarded -10.53)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 0.573392780421
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 0.5733927804213148, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent properly idled at a red light. (rewarded 0.57)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None, None)
0.580690023123
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 1.16656996185
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.1665699618503858, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.17)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None, 'left')
New state created!
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 0.593543850175
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.5935438501747696, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 0.59)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 4
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (5, 4), deadline = 25
0.8
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', 'forward', 'left')
New state created!
0.0
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 1.97058928941
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward', 'left'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.970589289407888, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward', 'left')
Agent followed the waypoint right. (rewarded 1.97)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None, None)
0.701157952505
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 1.7945923442
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.7945923442020262, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded 1.79)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'right', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.11135114267
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.111351142666752, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right', None)
Agent properly idled at a red light. (rewarded 2.11)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None, None)
New state created!
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: -9.74415907864
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -9.744159078639873, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent attempted driving forward through a red light. (rewarded -9.74)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: left, reward: -9.02437773974
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -9.024377739741906, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -9.02)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', 'right', None)
New state created!
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 1.62482226261
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.6248222626076503, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right', None)
Agent drove right instead of left. (rewarded 1.62)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None, None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: -5.56355436691
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': -5.563554366914795, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.56)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None, None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: -10.7763269131
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': -10.776326913080858, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -10.78)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None, 'forward')
New state created!
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: -39.8880259366
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None, 'forward'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -39.888025936589635, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.89)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 1.09941742918
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.0994174291787377, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.10)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: -10.794669831
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -10.794669831040938, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -10.79)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 0.989629198686
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 0.9896291986858535, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent idled at a green light with oncoming traffic. (rewarded 0.99)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'left', None, 'forward')
New state created!
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 1.55538799729
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, 'forward'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.5553879972902476, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, 'forward')
Agent followed the waypoint right. (rewarded 1.56)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.28760406998
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': 1.2876040699798033, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.29)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 0.546804527492
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.5468045274921732, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent drove right instead of forward. (rewarded 0.55)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: left, reward: -10.588469647
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -10.588469646998796, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -10.59)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None, None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 8.19702820437e-05
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 8.197028204370849e-05, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove right instead of left. (rewarded 0.00)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: 1.35346854344
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 1.3534685434430584, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', None)
Agent drove left instead of right. (rewarded 1.35)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'forward', None, 'forward')
New state created!
0.0
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: -19.8931718405
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'forward', None, 'forward'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': -19.893171840535665, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.89)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'forward', None, None)
0.81127093628
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 0.480797134251
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 0.48079713425127546, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove right instead of left. (rewarded 0.48)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', 'left', None, 'forward')
New state created!
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 1.13441128834
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, 'forward'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.13441128834209, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, 'forward')
Agent drove right instead of left. (rewarded 1.13)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', 'forward', None, None)
New state created!
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: -5.7511158764
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None, None), 'deadline': 4, 't': 21, 'action': None, 'reward': -5.751115876399492, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.75)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: -10.0920840493
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': -10.09208404928591, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -10.09)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 1.80422910606
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 2, 't': 23, 'action': None, 'reward': 1.8042291060640974, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.80)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, None, 'right')
New state created!
0.0
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 0.165054354857
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'right'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.16505435485685527, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None, 'right')
Agent followed the waypoint right. (rewarded 0.17)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 5
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (4, 5), deadline = 30
0.75
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: -39.9848645289
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', 'forward', 'left'), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': -39.984864528889446, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward', 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.98)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', 'forward', 'left')
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: -39.7050783768
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', 'forward', 'left'), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': -39.70507837682299, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward', 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.71)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None, 'left')
New state created!
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.55963706532
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, 'left'), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.559637065324087, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, 'left')
Agent properly idled at a red light. (rewarded 2.56)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: -10.1606830283
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 27, 't': 3, 'action': 'left', 'reward': -10.16068302829738, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -10.16)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'left', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.12947266019
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 1.129472660192149, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.13)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None, None)
1.21653628531
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 1.44710235288
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 1.4471023528797524, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.45)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 1.63636867243
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 1.6363686724312414, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent drove right instead of forward. (rewarded 1.64)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None, None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: left, reward: 1.73471296517
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 1.734712965169012, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 1.73)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', None, None)
New state created!
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: right, reward: 0.99186486582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None, None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 0.9918648658200543, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None, None)
Agent drove right instead of forward. (rewarded 0.99)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None, None)
0.690939193931
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: None, reward: 2.32832887425
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.328328874250223, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent properly idled at a red light. (rewarded 2.33)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'forward', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: forward, reward: 1.49958755152
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 1.4995875515216839, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove forward instead of left. (rewarded 1.50)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None, None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: forward, reward: -0.0671679929719
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': -0.0671679929719341, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove forward instead of left. (rewarded -0.07)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None, None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: forward, reward: -10.1061609132
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': -10.106160913209388, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent attempted driving forward through a red light. (rewarded -10.11)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None, None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 0.0165312663722
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 0.016531266372239894, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove right instead of left. (rewarded 0.02)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: 0.887814427375
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right', None), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 0.88781442737464, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right', None)
Agent drove right instead of left. (rewarded 0.89)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: -9.27141919986
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -9.271419199859805, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -9.27)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None, 'right')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 2.05479118266
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'right'), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 2.0547911826550083, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'right')
Agent followed the waypoint right. (rewarded 2.05)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None, None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 0.0285143683295
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 0.028514368329474404, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent drove right instead of forward. (rewarded 0.03)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None, None)
0.459877894732
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 0.886298122625
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 12, 't': 18, 'action': None, 'reward': 0.8862981226254667, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.89)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: forward, reward: 1.48073137101
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 11, 't': 19, 'action': 'forward', 'reward': 1.4807313710120362, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove forward instead of left. (rewarded 1.48)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', 'left', 'forward', 'forward')
New state created!
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: left, reward: 1.31610844058
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward', 'forward'), 'deadline': 10, 't': 20, 'action': 'left', 'reward': 1.31610844058394, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward', 'forward')
Agent followed the waypoint left. (rewarded 1.32)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: forward, reward: 2.02556535601
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None, None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 2.0255653560122155, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None, None)
Agent followed the waypoint forward. (rewarded 2.03)
27% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 6
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (8, 4), deadline = 20
0.7
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 0.579050418564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 0.5790504185639609, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right', None)
Agent drove right instead of forward. (rewarded 0.58)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None, None)
0.673088008679
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 2.67790425407
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.6779042540708042, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.68)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None, None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 1.62057786255
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.6205778625534282, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent drove right instead of left. (rewarded 1.62)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None, None)
0.314033659566
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: left, reward: 1.84330144408
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.8433014440813174, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.84)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: forward, reward: 0.741026164512
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 0.7410261645116286, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove forward instead of left. (rewarded 0.74)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None, None)
0.749793775761
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: forward, reward: 1.26867100335
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.268671003347048, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove forward instead of left. (rewarded 1.27)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None, None)
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: right, reward: -0.0364470271361
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': -0.036447027136119914, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent drove right instead of left. (rewarded -0.04)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: left, reward: 0.24193811677
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 0.24193811677005272, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 0.24)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None, None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: left, reward: 2.13531448825
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 2.1353144882546795, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 2.14)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', 'right', None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: left, reward: -10.2754334758
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -10.275433475774966, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right', None)
Agent attempted driving left through a red light. (rewarded -10.28)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 2.02482517873
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward', 'left'), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.0248251787305183, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.02)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None, 'left')
New state created!
0.0
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 1.09495684252
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None, 'left'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.0949568425153893, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None, 'left')
Agent drove right instead of forward. (rewarded 1.09)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: 0.600335920874
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.6003359208736032, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove forward instead of left. (rewarded 0.60)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 0.497415809283
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.49741580928346385, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove right instead of left. (rewarded 0.50)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'right', 'left', None)
New state created!
0.0
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 0.556777076013
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'left', None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.5567770760125903, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left', None)
Agent followed the waypoint right. (rewarded 0.56)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'left', None, None)
0.746032533781
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 1.10882388828
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.1088238882782306, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.11)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', 'left', None)
New state created!
0.0
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 0.64513886377
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left', None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 0.6451388637702855, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left', None)
Agent drove right instead of forward. (rewarded 0.65)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: forward, reward: 1.30359541896
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 1.3035954189629693, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove forward instead of left. (rewarded 1.30)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, 'right', 'right')
New state created!
0.0
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: right, reward: 1.20622212018
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right', 'right'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.206222120179964, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right', 'right')
Agent drove right instead of left. (rewarded 1.21)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, 'forward', 'forward')
0.138614581516
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: -0.15196791993
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', 'forward'), 'deadline': 1, 't': 19, 'action': 'right', 'reward': -0.15196791992997738, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, 'forward', 'forward')
Agent drove right instead of left. (rewarded -0.15)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 7
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (5, 4), deadline = 25
0.65
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'left', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 2.28234877296
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left', 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 2.282348772958703, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left', 'forward')
Agent followed the waypoint right. (rewarded 2.28)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None, 'forward')
New state created!
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: -20.4192844833
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, None, 'forward'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': -20.41928448334584, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.42)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None, None)
1.11192381167
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 1.62879838166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.6287983816559088, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent drove right instead of forward. (rewarded 1.63)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'forward', None)
New state created!
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: right, reward: 1.91927614101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.9192761410076946, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', None)
Agent drove right instead of left. (rewarded 1.92)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: 1.41708530415
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, 'forward'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.4170853041451656, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, 'forward')
Agent drove left instead of right. (rewarded 1.42)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'left', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: -20.3192794146
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'left', 'forward'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': -20.319279414551865, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.32)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left', None)
1.18530838497
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 1.22229130764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.2222913076386572, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 1.22)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'right', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: 1.84098258589
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right', None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 1.840982585890096, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right', None)
Agent followed the waypoint left. (rewarded 1.84)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None, None)
1.01464573428
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: 1.24031734593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.2403173459296497, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 1.24)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None, 'right')
New state created!
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: -5.03740348139
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None, 'right'), 'deadline': 16, 't': 9, 'action': None, 'reward': -5.037403481387806, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.04)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None, 'right')
New state created!
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: -10.7458097938
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None, 'right'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': -10.745809793819777, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'right')
Agent attempted driving forward through a red light. (rewarded -10.75)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None, None)
New state created!
0.0
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 1.59679609424
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.596796094241029, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent drove right instead of forward. (rewarded 1.60)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'left', None, 'forward')
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 1.3336330733
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, 'forward'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.3336330732996649, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, 'forward')
Agent drove right instead of left. (rewarded 1.33)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, 'left', 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 1.78282403325
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', 'forward'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 1.7828240332521081, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.78)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, 'forward', None)
New state created!
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 1.76910850688
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.769108506875249, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', None)
Agent followed the waypoint right. (rewarded 1.77)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'forward', 'forward', 'left')
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 1.20420199681
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward', 'left'), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': 1.2042019968069668, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.20)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'left', None)
New state created!
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: left, reward: -9.28006344155
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left', None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -9.280063441546908, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', None)
Agent attempted driving left through a red light. (rewarded -9.28)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: right, reward: 0.884931835145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward', None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 0.8849318351445976, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward', None)
Agent drove right instead of forward. (rewarded 0.88)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'forward', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: left, reward: -10.9499005792
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 7, 't': 18, 'action': 'left', 'reward': -10.949900579178347, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent attempted driving left through a red light. (rewarded -10.95)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: right, reward: 1.40338853032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward', None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.403388530324161, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward', None)
Agent drove right instead of left. (rewarded 1.40)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'green', None, None, 'forward')
0.842736805896
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: right, reward: 1.14913000127
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, 'forward'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.1491300012652963, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, 'forward')
Agent followed the waypoint right. (rewarded 1.15)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: forward, reward: -10.3516197197
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': -10.351619719729541, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -10.35)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', 'left', None, None)
0.92742821103
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: right, reward: 1.2377707182
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 1.2377707181984436, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.24)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, None, 'left')
New state created!
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: left, reward: -9.70700815917
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None, 'left'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -9.707008159174324, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'left')
Agent attempted driving left through a red light. (rewarded -9.71)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 0.711042824773
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 1, 't': 24, 'action': None, 'reward': 0.7110428247734886, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.71)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 8
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (8, 5), deadline = 35
0.6
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'left', None)
New state created!
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: right, reward: 0.0171946783419
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left', None), 'deadline': 35, 't': 0, 'action': 'right', 'reward': 0.017194678341889924, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left', None)
Agent drove right instead of left. (rewarded 0.02)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None, None)
1.3318193191
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: 1.23438143528
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 34, 't': 1, 'action': 'right', 'reward': 1.2343814352758817, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.23)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None, None)
0.798398047121
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 0.608248086184
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 33, 't': 2, 'action': 'right', 'reward': 0.6082480861843969, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent drove right instead of forward. (rewarded 0.61)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: -10.1419874551
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 32, 't': 3, 'action': 'left', 'reward': -10.141987455078747, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -10.14)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 1.6637760352
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 31, 't': 4, 'action': None, 'reward': 1.6637760352021078, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.66)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'right', 'right')
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 2.92971010204
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right', 'right'), 'deadline': 30, 't': 5, 'action': None, 'reward': 2.9297101020385874, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right', 'right')
Agent properly idled at a red light. (rewarded 2.93)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None, 'forward')
0.64317167532
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: 0.494155593909
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'forward'), 'deadline': 29, 't': 6, 'action': 'forward', 'reward': 0.4941555939088602, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'forward')
Agent drove forward instead of left. (rewarded 0.49)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'right', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 0.179183306413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right', 'forward'), 'deadline': 28, 't': 7, 'action': 'right', 'reward': 0.1791833064133891, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right', 'forward')
Agent drove right instead of left. (rewarded 0.18)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None, 'right')
New state created!
0.0
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 0.432968845297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, 'right'), 'deadline': 27, 't': 8, 'action': 'right', 'reward': 0.43296884529715685, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, 'right')
Agent drove right instead of left. (rewarded 0.43)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 1.91006381669
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward', None), 'deadline': 26, 't': 9, 'action': 'right', 'reward': 1.9100638166854038, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward', None)
Agent followed the waypoint right. (rewarded 1.91)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', 'right', None)
0.289525209282
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 0.00955944738328
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right', None), 'deadline': 25, 't': 10, 'action': 'right', 'reward': 0.009559447383284403, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right', None)
Agent drove right instead of forward. (rewarded 0.01)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 1.09792316611
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward', None), 'deadline': 24, 't': 11, 'action': 'right', 'reward': 1.0979231661131639, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward', None)
Agent drove right instead of left. (rewarded 1.10)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'forward', None, None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: -0.072048519872
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 23, 't': 12, 'action': 'forward', 'reward': -0.07204851987204897, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove forward instead of left. (rewarded -0.07)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: -10.7371525107
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 22, 't': 13, 'action': 'left', 'reward': -10.737152510733285, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -10.74)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'forward', 'right')
New state created!
0.0
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: right, reward: 1.37273365634
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward', 'right'), 'deadline': 21, 't': 14, 'action': 'right', 'reward': 1.3727336563443298, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward', 'right')
Agent drove right instead of forward. (rewarded 1.37)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', None, None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: right, reward: 0.335247849287
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 20, 't': 15, 'action': 'right', 'reward': 0.33524784928652795, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove right instead of left. (rewarded 0.34)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'left', None)
New state created!
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: left, reward: 0.0288938969014
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', None), 'deadline': 19, 't': 16, 'action': 'left', 'reward': 0.02889389690135158, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', None)
Agent drove left instead of forward. (rewarded 0.03)
51% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'left', 'left')
New state created!
0.0
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: 1.77980652944
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', 'left'), 'deadline': 18, 't': 17, 'action': 'right', 'reward': 1.7798065294446366, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', 'left')
Agent followed the waypoint right. (rewarded 1.78)
49% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', 'left', 'left', 'right')
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 1.4271742627
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left', 'right'), 'deadline': 17, 't': 18, 'action': None, 'reward': 1.4271742627023039, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left', 'right')
Agent idled at a green light with oncoming traffic. (rewarded 1.43)
46% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, 'left', 'left')
New state created!
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: 2.6741380447
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', 'left'), 'deadline': 16, 't': 19, 'action': 'forward', 'reward': 2.674138044698391, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.67)
43% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, 'right', 'left')
New state created!
0.0
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: -0.0627255759279
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right', 'left'), 'deadline': 15, 't': 20, 'action': 'right', 'reward': -0.06272557592791039, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right', 'left')
Agent drove right instead of forward. (rewarded -0.06)
40% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, 'forward', 'left')
New state created!
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 0.591261182035
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', 'left'), 'deadline': 14, 't': 21, 'action': 'right', 'reward': 0.5912611820352967, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', 'left')
Agent drove right instead of left. (rewarded 0.59)
37% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 0.736096626994
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 13, 't': 22, 'action': 'forward', 'reward': 0.7360966269937859, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove forward instead of left. (rewarded 0.74)
34% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'left', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: right, reward: 0.990518422738
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 12, 't': 23, 'action': 'right', 'reward': 0.99051842273764, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove right instead of left. (rewarded 0.99)
31% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, 'left', 'left')
New state created!
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: forward, reward: 1.15968262854
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', 'left'), 'deadline': 11, 't': 24, 'action': 'forward', 'reward': 1.1596826285376793, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', 'left')
Agent drove forward instead of right. (rewarded 1.16)
29% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'red', None, 'forward', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: right, reward: -19.7835359931
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward', 'forward'), 'deadline': 10, 't': 25, 'action': 'right', 'reward': -19.783535993057665, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.78)
26% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'red', None, 'forward', None)
0.884554253438
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 0.602636308083
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', None), 'deadline': 9, 't': 26, 'action': 'right', 'reward': 0.6026363080825972, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', None)
Agent followed the waypoint right. (rewarded 0.60)
23% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'red', 'left', None, None)
1.24787514835
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: right, reward: -0.0603633158585
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 8, 't': 27, 'action': 'right', 'reward': -0.0603633158585416, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded -0.06)
20% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'red', 'left', None, None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: right, reward: 1.30445732009
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 7, 't': 28, 'action': 'right', 'reward': 1.304457320090796, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 1.30)
17% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'red', None, None, 'left')
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: -10.9039465065
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 6, 't': 29, 'action': 'forward', 'reward': -10.903946506487964, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.90)
14% of time remaining to reach destination.
/-------------------
| Step 30 Results
\-------------------
Environment.step(): t = 30
('right', 'red', 'right', None, None)
New state created!
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: right, reward: 1.33703201196
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None, None), 'deadline': 5, 't': 30, 'action': 'right', 'reward': 1.337032011960363, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None, None)
Agent followed the waypoint right. (rewarded 1.34)
11% of time remaining to reach destination.
/-------------------
| Step 31 Results
\-------------------
Environment.step(): t = 31
('right', 'red', None, None, 'left')
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: None, reward: 2.20179617891
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 4, 't': 31, 'action': None, 'reward': 2.2017961789115748, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 2.20)
9% of time remaining to reach destination.
/-------------------
| Step 32 Results
\-------------------
Environment.step(): t = 32
('right', 'red', 'forward', None, 'right')
New state created!
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: left, reward: -9.99842246041
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None, 'right'), 'deadline': 3, 't': 32, 'action': 'left', 'reward': -9.998422460410692, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, 'right')
Agent attempted driving left through a red light. (rewarded -10.00)
6% of time remaining to reach destination.
/-------------------
| Step 33 Results
\-------------------
Environment.step(): t = 33
('right', 'red', 'left', None, 'forward')
New state created!
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: right, reward: -19.5865997655
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'left', None, 'forward'), 'deadline': 2, 't': 33, 'action': 'right', 'reward': -19.586599765467508, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.59)
3% of time remaining to reach destination.
/-------------------
| Step 34 Results
\-------------------
Environment.step(): t = 34
('right', 'green', 'left', None, None)
1.08259946461
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 0.772177655613
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 1, 't': 34, 'action': 'right', 'reward': 0.772177655613222, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 0.77)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 9
\-------------------------
Environment.reset(): Trial set up with start = (1, 6), destination = (6, 5), deadline = 20
0.55
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'left', None)
New state created!
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: forward, reward: 0.288299281899
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left', None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 0.28829928189882437, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left', None)
Agent drove forward instead of right. (rewarded 0.29)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'left', 'forward')
0.891412016626
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 1.38537960845
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.385379608445953, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.39)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.46185600252
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.4618560025202005, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.46)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None, None)
1.5696392162
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.57520974039
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.5752097403878933, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.58)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None, 'left')
New state created!
0.0
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 0.745447966516
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'left'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.7454479665159092, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'left')
Agent drove right instead of forward. (rewarded 0.75)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None, None)
1.66963608329
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.15994974357
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.1599497435666057, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.16)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None, None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 0.0824819812898
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.0824819812897859, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent drove right instead of left. (rewarded 0.08)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None, None)
1.50133548542
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: left, reward: 0.900308314251
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 0.9003083142509907, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 0.90)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'left', None)
1.20379984631
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: None, reward: 2.09808941388
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.0980894138753143, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 2.10)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: -9.45738551178
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': -9.457385511778341, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -9.46)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward', None)
0.959638070504
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: right, reward: -0.0827466575739
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': -0.08274665757394317, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', None)
Agent drove right instead of left. (rewarded -0.08)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'forward', 'left')
New state created!
0.0
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: right, reward: 1.60644881671
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', 'left'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.606448816713567, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', 'left')
Agent followed the waypoint right. (rewarded 1.61)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'forward', 'left', 'right')
New state created!
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: 0.680922119327
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left', 'right'), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.680922119327066, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left', 'right')
Agent properly idled at a red light. (rewarded 0.68)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: left, reward: -20.506156288
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -20.506156287951413, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None, None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.51)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'forward', 'right')
New state created!
0.0
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: 1.97823534514
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward', 'right'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.9782353451403072, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward', 'right')
Agent followed the waypoint right. (rewarded 1.98)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None, 'left')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: 1.45219806307
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'left'), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 1.4521980630703577, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'left')
Agent drove left instead of forward. (rewarded 1.45)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, 'forward', 'forward')
0.0
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: -39.4051790397
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward', 'forward'), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -39.40517903965289, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.41)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 1.78548717099
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.7854871709881421, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.79)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None, 'left')
1.249284052
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 0.963910658813
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.9639106588131061, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 0.96)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None, None)
0.660302834297
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: left, reward: -0.113697268383
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -0.11369726838345084, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded -0.11)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 10
\-------------------------
Environment.reset(): Trial set up with start = (4, 6), destination = (2, 3), deadline = 25
0.5
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None, None)
1.20082189984
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: left, reward: 1.08252043019
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.0825204301881113, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 1.08)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: 1.54858201938
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.5485820193757884, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded 1.55)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'right', None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: None, reward: 1.31133899836
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.3113389983552883, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right', None)
Agent properly idled at a red light. (rewarded 1.31)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: forward, reward: -9.29211567526
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -9.292115675259144, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -9.29)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None, 'left')
New state created!
0.0
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: right, reward: 0.708166973954
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'left'), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.7081669739539028, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'left')
Agent drove right instead of left. (rewarded 0.71)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'left', None)
New state created!
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: forward, reward: 1.49984249588
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.4998424958768104, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent drove forward instead of left. (rewarded 1.50)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None, 'forward')
New state created!
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: -39.2779785955
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None, 'forward'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -39.27797859550061, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.28)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None, 'left')
0.268194954413
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: forward, reward: 0.365494588001
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'left'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 0.3654945880007976, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'left')
Agent drove forward instead of left. (rewarded 0.37)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'right', None, 'left')
New state created!
0.0
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: right, reward: 0.980009062299
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None, 'left'), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 0.9800090622988132, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None, 'left')
Agent drove right instead of forward. (rewarded 0.98)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, 'left', 'forward')
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: forward, reward: -40.1715963958
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'left', 'forward'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': -40.171596395815925, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.17)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None, 'left')
0.354083486977
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 0.221059319479
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'left'), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 0.22105931947878432, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'left')
Agent drove right instead of left. (rewarded 0.22)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.69935898652
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.6993589865153942, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.70)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 2.07663991264
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.076639912644093, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.08)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'right', None, None)
New state created!
0.0
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 1.3339545816
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None, None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 1.3339545816001348, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None, None)
Agent drove right instead of forward. (rewarded 1.33)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'right', None, None)
0.0
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 1.50168022982
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.5016802298199412, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None, None)
Agent drove right instead of left. (rewarded 1.50)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None, 'right')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: 1.37602131946
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'right'), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': 1.3760213194597972, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'right')
Agent followed the waypoint forward. (rewarded 1.38)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: left, reward: -10.6795067806
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left', None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -10.67950678064584, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', None)
Agent attempted driving left through a red light. (rewarded -10.68)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'right', None, 'left')
0.490004531149
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 1.08436955968
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None, 'left'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 1.0843695596825962, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None, 'left')
Agent drove right instead of forward. (rewarded 1.08)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'forward', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: None, reward: 1.92193594834
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left', None), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.9219359483437723, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left', None)
Agent properly idled at a red light. (rewarded 1.92)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', None, None)
1.50963403409
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: None, reward: 2.25632641218
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 2.2563264121780495, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent properly idled at a red light. (rewarded 2.26)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 0.799933974601
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 0.7999339746011283, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent drove right instead of left. (rewarded 0.80)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', None, None, 'left')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -10.9779041472
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, 'left'), 'deadline': 4, 't': 21, 'action': 'left', 'reward': -10.97790414723479, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'left')
Agent attempted driving left through a red light. (rewarded -10.98)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'red', None, None, 'left')
0.287571403228
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 0.432491088648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'left'), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 0.4324910886476665, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'left')
Agent drove right instead of left. (rewarded 0.43)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', None, 'forward', 'right')
0.98911767257
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 2.17985705536
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward', 'right'), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 2.17985705536309, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward', 'right')
Agent followed the waypoint right. (rewarded 2.18)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: -9.33782416818
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': -9.337824168178827, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -9.34)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 11
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (2, 7), deadline = 35
0.45
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'forward', 'left')
0.803224408357
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 1.47516670612
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', 'left'), 'deadline': 35, 't': 0, 'action': 'right', 'reward': 1.475166706117348, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', 'left')
Agent followed the waypoint right. (rewarded 1.48)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None, 'left')
New state created!
0.0
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 1.03345280905
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, 'left'), 'deadline': 34, 't': 1, 'action': 'right', 'reward': 1.0334528090476296, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, 'left')
Agent followed the waypoint right. (rewarded 1.03)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: -10.3932078324
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 33, 't': 2, 'action': 'forward', 'reward': -10.393207832410168, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent attempted driving forward through a red light. (rewarded -10.39)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: -9.68859059258
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 32, 't': 3, 'action': 'left', 'reward': -9.68859059257779, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent attempted driving left through a red light. (rewarded -9.69)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None, None)
New state created!
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 1.94809474682
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None, None), 'deadline': 31, 't': 4, 'action': 'right', 'reward': 1.948094746817785, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None, None)
Agent drove right instead of forward. (rewarded 1.95)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward', 'left')
New state created!
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: forward, reward: -40.7269939982
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward', 'left'), 'deadline': 30, 't': 5, 'action': 'forward', 'reward': -40.72699399822199, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.73)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'forward', None, 'left')
New state created!
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: 1.06599064125
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, 'left'), 'deadline': 29, 't': 6, 'action': 'right', 'reward': 1.065990641252451, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, 'left')
Agent drove right instead of left. (rewarded 1.07)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'forward', None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: None, reward: -5.30120366104
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward', None), 'deadline': 28, 't': 7, 'action': None, 'reward': -5.30120366104396, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.30)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'forward', None)
0.955031908343
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 1.9043542775
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward', None), 'deadline': 27, 't': 8, 'action': 'right', 'reward': 1.9043542774955322, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward', None)
Agent followed the waypoint right. (rewarded 1.90)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None, None)
0.273302782957
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: left, reward: 1.31190403781
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 26, 't': 9, 'action': 'left', 'reward': 1.3119040378070488, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.31)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None, 'right')
0.0
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 0.29170825004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, 'right'), 'deadline': 25, 't': 10, 'action': 'right', 'reward': 0.29170825003998, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'right')
Agent drove right instead of forward. (rewarded 0.29)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: 1.0910835273
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 24, 't': 11, 'action': 'forward', 'reward': 1.0910835273020942, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove forward instead of left. (rewarded 1.09)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, 'right', None)
1.18350728484
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 2.65221127598
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right', None), 'deadline': 23, 't': 12, 'action': None, 'reward': 2.652211275981961, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right', None)
Agent properly idled at a red light. (rewarded 2.65)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'right', None, 'forward')
New state created!
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: 1.52540103983
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None, 'forward'), 'deadline': 22, 't': 13, 'action': 'right', 'reward': 1.5254010398297453, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None, 'forward')
Agent drove right instead of left. (rewarded 1.53)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'left', 'forward')
New state created!
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 1.43078304416
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', 'forward'), 'deadline': 21, 't': 14, 'action': 'right', 'reward': 1.4307830441555942, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', 'forward')
Agent drove right instead of forward. (rewarded 1.43)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'right', 'left')
New state created!
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: -9.98388613257
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right', 'left'), 'deadline': 20, 't': 15, 'action': 'left', 'reward': -9.983886132574119, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right', 'left')
Agent attempted driving left through a red light. (rewarded -9.98)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None, None)
1.14167116501
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: left, reward: 1.58387651006
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 19, 't': 16, 'action': 'left', 'reward': 1.583876510059184, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 1.58)
51% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'left', 'left')
1.33706902235
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 1.46009522162
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', 'left'), 'deadline': 18, 't': 17, 'action': 'forward', 'reward': 1.4600952216169207, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.46)
49% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'left', None)
0.0144469484507
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: 0.755594721978
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', None), 'deadline': 17, 't': 18, 'action': 'left', 'reward': 0.7555947219779049, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', None)
Agent drove left instead of forward. (rewarded 0.76)
46% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'left', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.32124711087
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left', None), 'deadline': 16, 't': 19, 'action': None, 'reward': 1.3212471108748982, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.32)
43% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'green', 'left', 'left', None)
0.660623555437
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.67403093147
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left', None), 'deadline': 15, 't': 20, 'action': None, 'reward': 1.6740309314680606, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.67)
40% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', None, 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: -4.07022824614
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 14, 't': 21, 'action': None, 'reward': -4.070228246135565, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.07)
37% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', 'left', 'left', 'right')
New state created!
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: -9.73426021316
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', 'left', 'right'), 'deadline': 13, 't': 22, 'action': 'left', 'reward': -9.734260213159583, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -9.73)
34% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', 'left', 'left', None)
New state created!
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: -9.32417518795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', 'left', None), 'deadline': 12, 't': 23, 'action': 'forward', 'reward': -9.324175187947743, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.32)
31% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', 'left', 'left', 'forward')
New state created!
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: -39.7161779646
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', 'left', 'forward'), 'deadline': 11, 't': 24, 'action': 'forward', 'reward': -39.7161779646121, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.72)
29% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'green', 'left', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 0.100205117719
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left', None), 'deadline': 10, 't': 25, 'action': None, 'reward': 0.10020511771900265, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.10)
26% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'green', 'left', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: 0.614604049689
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left', None), 'deadline': 9, 't': 26, 'action': 'left', 'reward': 0.614604049689226, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left', None)
Agent drove left instead of right. (rewarded 0.61)
23% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'red', 'forward', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 1.86725138079
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None, None), 'deadline': 8, 't': 27, 'action': 'right', 'reward': 1.8672513807885283, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, None)
Agent followed the waypoint right. (rewarded 1.87)
20% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'red', None, 'right', None)
0.461128919766
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: None, reward: 1.04742386285
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right', None), 'deadline': 7, 't': 28, 'action': None, 'reward': 1.0474238628457706, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right', None)
Agent properly idled at a red light. (rewarded 1.05)
17% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'green', None, 'left', None)
0.676734271722
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -0.41582490076
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 6, 't': 29, 'action': 'left', 'reward': -0.41582490076046985, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', None)
Agent drove left instead of right. (rewarded -0.42)
14% of time remaining to reach destination.
/-------------------
| Step 30 Results
\-------------------
Environment.step(): t = 30
('left', 'red', 'left', 'right', 'left')
New state created!
0.0
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 0.771281785502
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right', 'left'), 'deadline': 5, 't': 30, 'action': 'right', 'reward': 0.7712817855015957, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right', 'left')
Agent drove right instead of left. (rewarded 0.77)
11% of time remaining to reach destination.
/-------------------
| Step 31 Results
\-------------------
Environment.step(): t = 31
('right', 'red', None, 'forward', None)
0.74359528076
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 1.61263309707
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', None), 'deadline': 4, 't': 31, 'action': 'right', 'reward': 1.6126330970661356, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', None)
Agent followed the waypoint right. (rewarded 1.61)
9% of time remaining to reach destination.
/-------------------
| Step 32 Results
\-------------------
Environment.step(): t = 32
('forward', 'red', None, None, None)
1.98126582252
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 0.41545838681
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 3, 't': 32, 'action': None, 'reward': 0.41545838680989666, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.42)
6% of time remaining to reach destination.
/-------------------
| Step 33 Results
\-------------------
Environment.step(): t = 33
('forward', 'green', 'right', None, None)
0.6669772908
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: -0.0241827481156
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None, None), 'deadline': 2, 't': 33, 'action': 'right', 'reward': -0.024182748115584296, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None, None)
Agent drove right instead of forward. (rewarded -0.02)
3% of time remaining to reach destination.
/-------------------
| Step 34 Results
\-------------------
Environment.step(): t = 34
('left', 'red', None, 'forward', None)
0.438445706465
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 0.886520686521
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', None), 'deadline': 1, 't': 34, 'action': 'right', 'reward': 0.8865206865209245, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, 'forward', None)
Agent drove right instead of left. (rewarded 0.89)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 12
\-------------------------
Environment.reset(): Trial set up with start = (5, 6), destination = (2, 7), deadline = 20
0.4
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None, None)
1.36277383754
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: left, reward: 2.35067201987
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 2.3506720198703603, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 2.35)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None, 'right')
New state created!
0.0
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 1.68542464672
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, 'right'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.6854246467195186, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, 'right')
Agent drove right instead of forward. (rewarded 1.69)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None, 'forward')
New state created!
0.0
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: -19.1938500976
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'left', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': -19.19385009756088, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.19)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: -9.23129709916
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -9.231297099164102, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -9.23)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: -9.31794730839
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -9.317947308387302, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.32)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None, None)
0.652228660045
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 0.524219013845
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.5242190138450937, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 0.52)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.3165372005
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.3165372005041598, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right', None)
Agent followed the waypoint right. (rewarded 1.32)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'left', None)
0.130454685481
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: -0.0137122464902
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -0.01371224649015046, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', None)
Agent drove left instead of right. (rewarded -0.01)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: right, reward: 0.74699969214
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 0.7469996921398326, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent drove right instead of forward. (rewarded 0.75)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'forward', 'left')
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: right, reward: 1.13171481088
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', 'left'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.1317148108771018, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', 'left')
Agent drove right instead of left. (rewarded 1.13)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None, None)
1.1274815401
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 1.60973865331
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.6097386533103513, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 1.61)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None, None)
1.19836210467
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 0.916561047661
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.9165610476607784, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.92)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None, None)
1.0586803944
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: 1.43570153102
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.4357015310225263, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent drove right instead of forward. (rewarded 1.44)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None, 'left')
0.316844771207
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: 0.68647146999
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'left'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.6864714699904183, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'left')
Agent drove forward instead of left. (rewarded 0.69)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None, 'right')
0.0
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 0.819354664311
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'right'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.8193546643111745, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'right')
Agent drove right instead of left. (rewarded 0.82)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 0.822092514968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.8220925149680958, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward', None)
Agent followed the waypoint right. (rewarded 0.82)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None, 'left')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: -9.35366749896
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -9.353667498957757, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'left')
Agent attempted driving left through a red light. (rewarded -9.35)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'right', None)
0.754276391306
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.49008957856
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right', None), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.4900895785611754, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right', None)
Agent properly idled at a red light. (rewarded 1.49)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: -10.0779304931
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None, None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -10.077930493069381, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, None)
Agent attempted driving left through a red light. (rewarded -10.08)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'forward', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: -4.36257314453
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': -4.36257314453276, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'forward', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.36)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 13
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (5, 7), deadline = 20
0.35
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None, None)
1.91479291343
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.4669368804
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.4669368804004077, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.47)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: forward, reward: -10.6604983182
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, 'left'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -10.660498318205214, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.66)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'left', None)
1.65094463009
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 2.07704569752
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.077045697516952, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 2.08)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None, None)
1.69086489691
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 2.71969617307
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.719696173074335, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.72)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: 0.266872083483
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 0.26687208348263225, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove forward instead of left. (rewarded 0.27)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'left', None)
0.749921247938
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: 0.498406334285
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 0.4984063342852625, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent drove forward instead of left. (rewarded 0.50)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left', 'forward')
0.0
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: left, reward: -39.7161413438
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'left', 'forward'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -39.716141343759176, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.72)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None, 'forward')
0.0
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: -20.6884523783
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, None, 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': -20.688452378312462, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.69)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: left, reward: -10.5719053648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -10.571905364823834, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -10.57)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'right', None, None)
New state created!
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: left, reward: -39.5267639796
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -39.5267639796207, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.53)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'right', 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: -0.202149414213
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'forward', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': -0.20214941421272903, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward', None)
Agent drove right instead of left. (rewarded -0.20)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None, 'forward')
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: left, reward: -40.1711801326
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None, 'forward'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -40.17118013261936, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.17)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 1.25376567066
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.253765670656832, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.25)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None, 'left')
1.10659735541
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 0.745632233478
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.7456322334780603, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 0.75)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None, None)
1.34380086201
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.17649733093
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.1764973309264068, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.18)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None, None)
1.26843302392
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 2.28746522333
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 2.287465223331241, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 2.29)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'left', 'forward')
0.842174107531
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.88500919619
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left', 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.8850091961885547, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.89)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None, 'left')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.62247333139
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, 'left'), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.6224733313944957, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 1.62)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 0.0147970701723
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.014797070172276605, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent drove right instead of forward. (rewarded 0.01)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', None, None)
0.588223836945
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: -0.171111557775
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': -0.1711115577746961, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded -0.17)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 14
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (4, 3), deadline = 25
0.3
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'left', None)
0.0583712194952
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: 0.511196668193
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 0.5111966681930016, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', None)
Agent drove left instead of right. (rewarded 0.51)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left', 'forward')
0.715391522078
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 0.579687644608
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', 'forward'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.5796876446078312, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', 'forward')
Agent drove right instead of forward. (rewarded 0.58)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None, None)
2.20528053499
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 1.20274345218
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.2027434521801503, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.20)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None, None)
1.70401199359
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.60436271554
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.6043627155426727, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.60)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'forward', None)
0.662483196493
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 0.38803035366
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.3880303536597801, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', None)
Agent drove right instead of left. (rewarded 0.39)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None, 'left')
0.811236665697
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.82291929069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, 'left'), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.8229192906874156, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 1.82)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'right', None, None)
0.49593243291
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 1.67298559801
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.6729855980072692, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None, None)
Agent drove right instead of forward. (rewarded 1.67)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'left', None)
1.8639951638
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 2.50325303942
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.503253039421458, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 2.50)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None, 'forward')
0.0
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: -40.887613485
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None, 'forward'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -40.88761348504357, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.89)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'right', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: -40.973201071
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', None, None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -40.97320107100653, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.97)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: -9.02003228188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -9.0200322818797, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent attempted driving left through a red light. (rewarded -9.02)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'forward', None)
New state created!
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: -4.91386861349
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'forward', None), 'deadline': 14, 't': 11, 'action': None, 'reward': -4.913868613494726, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.91)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'forward', 'forward')
0.0
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: 1.66006700177
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', 'forward'), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 1.660067001765715, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', 'forward')
Agent followed the waypoint left. (rewarded 1.66)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', 'forward', None)
0.577416655137
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: 1.65250848641
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.652508486412576, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.65)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None, None)
1.05746157616
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: 1.09451022032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.0945102203155723, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.09)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, 'left', None)
0.0
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: right, reward: 0.747740666259
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.74774066625927, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', None)
Agent drove right instead of forward. (rewarded 0.75)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None, 'forward')
0.0
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 1.13049407092
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'forward'), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.1304940709216917, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'forward')
Agent properly idled at a red light. (rewarded 1.13)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, None, None)
2.15418735456
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 0.900378253837
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 8, 't': 17, 'action': None, 'reward': 0.9003782538368406, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.90)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None, None)
1.5272828042
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 2.07099500393
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 2.0709950039306095, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.07)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None, None)
1.79913890407
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 1.48372753669
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.4837275366897718, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.48)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'left', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -9.4582762876
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'left', None), 'deadline': 5, 't': 20, 'action': 'left', 'reward': -9.4582762876015, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.46)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'left', 'forward', None)
New state created!
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: forward, reward: 0.921118197199
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward', None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.9211181971988894, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward', None)
Agent drove forward instead of left. (rewarded 0.92)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None, 'left')
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: -4.71848518996
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None, 'left'), 'deadline': 3, 't': 22, 'action': None, 'reward': -4.71848518996398, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.72)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'right', None, 'left')
New state created!
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: forward, reward: -0.253670414118
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None, 'left'), 'deadline': 2, 't': 23, 'action': 'forward', 'reward': -0.253670414117652, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None, 'left')
Agent drove forward instead of left. (rewarded -0.25)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'green', None, None, None)
1.8567229287
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: left, reward: 1.64255885052
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 1, 't': 24, 'action': 'left', 'reward': 1.6425588505227646, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 1.64)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 15
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (6, 7), deadline = 25
0.25
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, 'left', 'forward')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: -40.6458345282
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'left', 'forward'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -40.64583452821448, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.65)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 0.98023970655
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.9802397065498438, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', None)
Agent drove right instead of forward. (rewarded 0.98)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None, None)
1.64143322038
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 1.15035364957
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.1503536495662006, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.15)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None, None)
1.39589343497
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 1.89259609234
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.8925960923383298, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.89)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: forward, reward: -10.2155299433
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': -10.215529943326981, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent attempted driving forward through a red light. (rewarded -10.22)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 0.514041931899
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'left'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.5140419318987358, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'left')
Agent drove right instead of left. (rewarded 0.51)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None, None)
1.07598589824
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 2.24393744543
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.2439374454280703, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.24)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 1.09745720411
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None, None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.0974572041074167, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None, None)
Agent drove right instead of forward. (rewarded 1.10)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None, None)
0.208556139585
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 0.20299117163
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 0.20299117162972558, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 0.20)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None, 'forward')
0.0
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: -40.199039841
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None, 'forward'), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -40.19903984095229, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.20)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'left', None)
0.385020835214
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: 1.68114668213
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 1.6811466821343757, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', None)
Agent drove left instead of forward. (rewarded 1.68)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', None, None)
0.927388560114
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 2.2428729459
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 2.242872945903919, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 2.24)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: 1.0489313393
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.0489313393008688, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent drove right instead of forward. (rewarded 1.05)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: left, reward: -39.3341462886
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward', None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': -39.33414628861166, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.33)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', None, None)
0.0
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: right, reward: 0.655744397937
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.6557443979372147, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None, None)
Agent drove right instead of left. (rewarded 0.66)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None, None)
1.64424476366
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: 1.74047708388
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.7404770838791122, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.74)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None, 'forward')
0.568663634615
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: 1.10664198031
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'forward'), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 1.1066419803066272, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'forward')
Agent drove forward instead of left. (rewarded 1.11)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 0.633553446091
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right', None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 0.6335534460911296, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right', None)
Agent drove right instead of forward. (rewarded 0.63)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None, None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 0.848446653291
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 0.8484466532911608, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent drove right instead of left. (rewarded 0.85)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, None, 'left')
0.726099031535
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: left, reward: 1.46046643774
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'left'), 'deadline': 6, 't': 19, 'action': 'left', 'reward': 1.4604664377379417, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'left')
Agent drove left instead of forward. (rewarded 1.46)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', None, None, 'right')
1.06865918004
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 1.64525259132
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'right'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.64525259131872, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'right')
Agent followed the waypoint right. (rewarded 1.65)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', None, None, None)
1.65996167183
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.37067591309
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 4, 't': 21, 'action': None, 'reward': 1.3706759130873793, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.37)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None, 'forward')
0.287485622202
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: 0.831805130849
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'forward'), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 0.8318051308487107, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent drove left instead of forward. (rewarded 0.83)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, 'forward', 'left')
1.13919555724
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 1.84688095197
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', 'left'), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.8468809519678933, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', 'left')
Agent followed the waypoint right. (rewarded 1.85)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, None, None)
1.36861009671
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 1.95659721787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': 1.9565972178739015, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 1.96)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 16
\-------------------------
Environment.reset(): Trial set up with start = (1, 3), destination = (5, 5), deadline = 30
0.2
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'right', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 0.138505819601
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right', 'forward'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 0.1385058196013511, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right', 'forward')
Agent drove right instead of left. (rewarded 0.14)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None, 'forward')
New state created!
0.0
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: -20.6092164637
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'left', None, 'forward'), 'deadline': 29, 't': 1, 'action': 'right', 'reward': -20.609216463748723, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.61)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'left', None)
New state created!
0.0
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 0.230057181765
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left', None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.23005718176493406, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left', None)
Agent drove right instead of forward. (rewarded 0.23)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'left', None)
2.18362410161
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 1.33547863015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.3354786301506827, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 1.34)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None, None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 0.204124440005
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 0.20412444000512442, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove right instead of left. (rewarded 0.20)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None, None)
1.64940644708
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 2.7580413288
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None, None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 2.758041328796642, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None, None)
Agent followed the waypoint forward. (rewarded 2.76)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None, None)
1.66260365729
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 2.35423063245
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 2.3542306324508284, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 2.35)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None, 'forward')
0.559645376525
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: left, reward: 0.776441228312
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'forward'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 0.7764412283122967, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent drove left instead of forward. (rewarded 0.78)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'right', None, None)
0.66851600598
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 1.39098560402
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None, None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.39098560401882, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None, None)
Agent followed the waypoint right. (rewarded 1.39)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None, None)
1.51531879246
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.9189903089
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': 1.9189903088953488, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.92)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', None, None)
1.03575228876
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 0.157926468664
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None, None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 0.15792646866358107, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None, None)
Agent drove right instead of forward. (rewarded 0.16)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', 'left', None)
New state created!
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: 1.39344376022
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left', None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': 1.3934437602173468, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left', None)
Agent followed the waypoint left. (rewarded 1.39)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'left', None)
0.624163791112
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 1.07998082502
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 1.0799808250165635, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent drove forward instead of left. (rewarded 1.08)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None, None)
1.74964088961
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: left, reward: 1.25535354985
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 17, 't': 13, 'action': 'left', 'reward': 1.2553535498460209, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 1.26)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: left, reward: -10.1348418819
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 16, 't': 14, 'action': 'left', 'reward': -10.134841881916731, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -10.13)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: left, reward: -9.79902235657
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -9.799022356565708, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -9.80)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: forward, reward: -0.201754148891
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': -0.20175414889091559, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove forward instead of left. (rewarded -0.20)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'left', 'left')
New state created!
0.0
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: right, reward: 0.0477527633946
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', 'left'), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 0.04775276339464618, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', 'left')
Agent drove right instead of left. (rewarded 0.05)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'right', None, 'forward')
New state created!
0.0
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: 2.54188322176
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None, 'forward'), 'deadline': 12, 't': 18, 'action': 'right', 'reward': 2.5418832217609886, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None, 'forward')
Agent followed the waypoint right. (rewarded 2.54)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None, None)
1.77794912363
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: right, reward: 2.6172593182
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 11, 't': 19, 'action': 'right', 'reward': 2.617259318195967, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 2.62)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None, 'right')
0.84271232336
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: right, reward: 0.33519031642
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, 'right'), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 0.3351903164199965, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, 'right')
Agent drove right instead of forward. (rewarded 0.34)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None, None)
1.50249721973
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: left, reward: 0.820873150565
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 9, 't': 21, 'action': 'left', 'reward': 0.8208731505654543, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 0.82)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'left', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: right, reward: 0.699714616526
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 0.6997146165261163, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove right instead of left. (rewarded 0.70)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'right', 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: right, reward: 2.05438666066
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'right', None), 'deadline': 7, 't': 23, 'action': 'right', 'reward': 2.0543866606637344, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'right', None)
Agent followed the waypoint right. (rewarded 2.05)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', 'left', None, None)
0.873629992487
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: 1.70594622346
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 6, 't': 24, 'action': 'right', 'reward': 1.7059462234577751, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.71)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'green', None, 'forward', None)
1.42969309292
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 2.27845065125
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward', None), 'deadline': 5, 't': 25, 'action': 'right', 'reward': 2.2784506512548317, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward', None)
Agent followed the waypoint right. (rewarded 2.28)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'red', None, None, None)
1.69236092377
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 0.587190727554
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 4, 't': 26, 'action': None, 'reward': 0.5871907275543584, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.59)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'red', None, None, None)
1.13977582566
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 1.52099528924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 3, 't': 27, 'action': None, 'reward': 1.5209952892419862, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.52)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'green', None, 'right', None)
0.920491292945
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: 0.616341576617
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right', None), 'deadline': 2, 't': 28, 'action': 'left', 'reward': 0.6163415766171576, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right', None)
Agent followed the waypoint left. (rewarded 0.62)
3% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 17
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (6, 7), deadline = 20
0.15
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'forward', 'right')
New state created!
0.0
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 2.41216535995
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward', 'right'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.412165359949031, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward', 'right')
Agent followed the waypoint right. (rewarded 2.41)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left', None)
1.03308375867
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: 0.524522012242
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 0.5245220122424051, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', None)
Agent drove left instead of forward. (rewarded 0.52)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'left', 'left')
0.579841314269
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: forward, reward: 1.40858065414
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.4085806541390928, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', 'left')
Agent drove forward instead of right. (rewarded 1.41)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'forward', None, None)
0.0
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: right, reward: 1.14967198244
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.1496719824407617, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None, None)
Agent followed the waypoint right. (rewarded 1.15)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'forward', 'left')
New state created!
0.0
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: 1.81317149027
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward', 'left'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.8131714902651037, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward', 'left')
Agent drove right instead of forward. (rewarded 1.81)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None, None)
1.33038555745
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.470128724
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.4701287240038114, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.47)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None, None)
1.40025714073
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.78737901919
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.7873790191862184, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.79)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None, None)
1.16168518515
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: left, reward: 2.74079288138
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.7407928813760405, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 2.74)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'left', None)
0.836141155335
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 0.931433644187
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.9314336441868973, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 0.93)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, 'left', None)
0.883787399761
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 0.844781749266
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 0.8447817492660203, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 0.84)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None, None)
0.792603410382
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: left, reward: 1.26488581082
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 1.2648858108212462, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.26)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None, None)
2.19760422091
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: right, reward: 1.24568198034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.2456819803388763, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.25)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', None, 'left')
1.27981853266
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 0.704305982473
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, 'left'), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.7043059824730988, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, 'left')
Agent properly idled at a red light. (rewarded 0.70)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'left', None, None)
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 1.84866568711
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.8486656871086007, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent properly idled at a red light. (rewarded 1.85)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: right, reward: 0.738074792015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right', None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.7380747920146489, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right', None)
Agent followed the waypoint right. (rewarded 0.74)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None, None)
1.0287446106
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: left, reward: 0.989055828476
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 0.9890558284760578, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 0.99)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'left', None)
0.284783943844
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: left, reward: 0.676178562083
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 0.6761785620826208, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', None)
Agent drove left instead of right. (rewarded 0.68)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', None, None)
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: left, reward: -9.70902050721
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -9.709020507214184, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent attempted driving left through a red light. (rewarded -9.71)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None, None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 0.0751770604313
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.07517706043134198, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 0.08)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, 'forward', None)
1.85407187209
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: right, reward: 2.08412146235
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward', None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 2.084121462348332, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'forward', None)
Agent followed the waypoint right. (rewarded 2.08)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 18
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (5, 6), deadline = 25
0.1
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
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epsilon = 0.1000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None, 'forward')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: -39.599872266
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', None, 'forward'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': -39.59987226600354, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.60)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None, None)
1.07116896781
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: 0.198189844771
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.19818984477103085, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded 0.20)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: left, reward: 2.38718467935
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': 2.3871846793503106, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 2.39)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', 'right', None)
0.149542328333
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 1.50288347641
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.5028834764126349, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right', None)
Agent drove right instead of forward. (rewarded 1.50)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'forward', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: -19.2851586523
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward', 'forward'), 'deadline': 21, 't': 4, 'action': 'right', 'reward': -19.285158652338986, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.29)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None, 'left')
0.437036588918
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 1.55268575333
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'left'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.5526857533260707, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'left')
Agent drove right instead of left. (rewarded 1.55)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, 'forward', None)
0.0
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 1.19021234063
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.1902123406281664, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', None)
Agent drove right instead of left. (rewarded 1.19)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'left', None)
0.480481252963
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: left, reward: 0.102047123488
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 0.10204712348846301, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', None)
Agent drove left instead of right. (rewarded 0.10)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None, 'forward')
0.565247035461
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.0818348426
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.081834842595855, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'forward')
Agent properly idled at a red light. (rewarded 2.08)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None, None)
1.95123903326
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: left, reward: 2.50278939256
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 2.5027893925570117, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 2.50)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'left', None)
1.19359233968
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: left, reward: 0.941724969479
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 0.9417249694789798, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 0.94)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None, 'forward')
0.0
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: -40.9995827082
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None, 'forward'), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': -40.99958270824727, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -41.00)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, 'forward', None)
0.442465917572
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 1.07439722469
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward', None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.074397224689746, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward', None)
Agent drove right instead of forward. (rewarded 1.07)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', 'forward', None)
0.460559098599
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: forward, reward: 0.161129284043
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 0.16112928404307736, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward', None)
Agent drove forward instead of left. (rewarded 0.16)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', None, 'left')
New state created!
0.0
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 0.00196996200699
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, 'left'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.0019699620069882906, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, 'left')
Agent drove right instead of left. (rewarded 0.00)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', None, None)
1.28978810797
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 1.17686115684
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 1.1768611568363878, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.18)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, 'left', None)
0.864284574513
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 0.904030824777
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 0.904030824776926, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 0.90)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', 'forward', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: -19.341326213
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'left', 'forward', 'forward'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': -19.341326213038116, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.34)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', 'right', None)
0.369037396007
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 2.51323537653
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right', None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 2.5132353765274313, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right', None)
Agent followed the waypoint right. (rewarded 2.51)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'forward', None, 'forward')
New state created!
0.0
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: -20.4571909525
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', None, 'forward'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': -20.457190952480996, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.46)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'forward', None, None)
0.703323066652
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: -0.338204654164
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': -0.33820465416442547, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent drove right instead of forward. (rewarded -0.34)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None, None)
2.22701421291
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: left, reward: 0.600287216861
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 0.6002872168614948, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 0.60)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None, None)
2.00841714487
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: 1.90212384677
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 1.9021238467738544, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 1.90)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', None, None, 'left')
1.09328273464
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 0.819560751993
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'left'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': 0.8195607519929691, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'left')
Agent drove left instead of forward. (rewarded 0.82)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', 'left', None, None)
1.2333246324
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: right, reward: 1.51516660604
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 1.5151666060361388, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.52)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 19
\-------------------------
Environment.reset(): Trial set up with start = (4, 3), destination = (8, 5), deadline = 30
0.05
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
Simulating trial. . .
epsilon = 0.0500; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None, None)
1.41365071489
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: left, reward: 2.68812352474
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 2.6881235247410453, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 2.69)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left', 'forward')
0.647539583343
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 0.485228940777
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', 'forward'), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 0.48522894077693157, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', 'forward')
Agent drove right instead of forward. (rewarded 0.49)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'left', None)
1.06765865458
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: 1.0769723699
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 28, 't': 2, 'action': 'left', 'reward': 1.0769723699000695, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 1.08)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.82152411284
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward', 'forward'), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 1.8215241128397728, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward', 'forward')
Agent drove right instead of forward. (rewarded 1.82)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 1.3688763721
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right', None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 1.3688763721012491, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right', None)
Agent drove right instead of left. (rewarded 1.37)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'forward', 'left')
0.713672700947
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 1.67812178763
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', 'left'), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 1.678121787634106, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', 'left')
Agent drove right instead of left. (rewarded 1.68)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None, 'left')
0.926114794442
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 2.75792914924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.7579291492387243, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 2.76)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'left', 'right', 'left')
New state created!
0.0
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 2.83228085567
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right', 'left'), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 2.8322808556701675, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right', 'left')
Agent followed the waypoint right. (rewarded 2.83)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None, 'forward')
0.668043302419
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: 0.213626959234
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'forward'), 'deadline': 22, 't': 8, 'action': 'left', 'reward': 0.2136269592341623, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent drove left instead of forward. (rewarded 0.21)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'left', None, 'left')
0.992062257568
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 2.83014875123
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, 'left'), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.830148751229687, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, 'left')
Agent properly idled at a red light. (rewarded 2.83)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None, None)
1.37424561922
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 1.68359179623
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 1.6835917962320943, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.68)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None, None)
1.71715455068
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.53422898126
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 19, 't': 11, 'action': None, 'reward': 1.5342289812620133, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.53)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None, None)
0.182559206244
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: 0.524445385576
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 0.524445385575942, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent drove right instead of forward. (rewarded 0.52)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: right, reward: 0.380853045661
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 0.3808530456606096, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right', None)
Agent drove right instead of left. (rewarded 0.38)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', 'forward', 'left')
New state created!
0.0
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: right, reward: 1.46395000313
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward', 'left'), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 1.4639500031326746, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward', 'left')
Agent drove right instead of left. (rewarded 1.46)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'forward', None)
1.96909666722
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 1.28426960876
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward', None), 'deadline': 15, 't': 15, 'action': 'right', 'reward': 1.284269608764689, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward', None)
Agent followed the waypoint right. (rewarded 1.28)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'right', 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: 0.841117990648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'right', None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 0.8411179906480997, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right', None)
Agent drove right instead of forward. (rewarded 0.84)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', 'left', None)
New state created!
0.0
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: right, reward: 0.0640537767534
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left', None), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 0.06405377675340684, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left', None)
Agent drove right instead of left. (rewarded 0.06)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None, None)
1.59381807996
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: 1.25952779851
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 12, 't': 18, 'action': None, 'reward': 1.2595277985137974, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.26)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None, 'forward')
0.837652807461
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: 1.5289214045
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'forward'), 'deadline': 11, 't': 19, 'action': 'forward', 'reward': 1.528921404502472, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'forward')
Agent drove forward instead of left. (rewarded 1.53)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, 'left', 'right')
New state created!
0.0
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 1.45449191898
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', 'right'), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 1.4544919189836416, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', 'right')
Agent drove right instead of left. (rewarded 1.45)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, 'forward', None)
1.17811418891
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 1.52207969535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 1.5220796953527345, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', None)
Agent followed the waypoint right. (rewarded 1.52)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', 'left', 'left', None)
0.115028590882
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 0.203475710101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left', None), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 0.20347571010079735, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left', None)
Agent drove right instead of forward. (rewarded 0.20)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'forward', 'left', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 0.359491206066
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left', 'forward'), 'deadline': 7, 't': 23, 'action': 'right', 'reward': 0.35949120606597595, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left', 'forward')
Agent drove right instead of left. (rewarded 0.36)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'left', None, None)
0.140475358019
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 1.03329893291
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 6, 't': 24, 'action': 'right', 'reward': 1.0332989329115272, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 1.03)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'red', None, None, None)
1.72164310063
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 1.67846967917
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 5, 't': 25, 'action': 'right', 'reward': 1.678469679172443, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.68)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('forward', 'green', 'left', 'forward', None)
1.11496257077
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: forward, reward: 1.26920483689
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward', None), 'deadline': 4, 't': 26, 'action': 'forward', 'reward': 1.2692048368890716, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.27)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'red', None, None, None)
1.62569176597
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 2.20194890184
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 3, 't': 27, 'action': None, 'reward': 2.2019489018406073, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.20)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('forward', 'red', None, None, None)
1.91382033391
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 0.370254698488
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 2, 't': 28, 'action': None, 'reward': 0.3702546984881907, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.37)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'green', 'forward', None, 'left')
0.547478421258
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: -0.779165014998
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None, 'left'), 'deadline': 1, 't': 29, 'action': 'right', 'reward': -0.7791650149978315, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', 'forward', None, 'left')
Agent drove right instead of forward. (rewarded -0.78)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 20
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (8, 6), deadline = 25
-3.1918911958e-16
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
Simulating trial. . .
epsilon = -0.0000; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'left', None)
1.07231551224
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: 2.40884266932
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 2.4088426693167815, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 2.41)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None, None)
0.586887145465
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 1.08974445152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.0897444515190688, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 1.09)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'left', None)
1.74057909078
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: 1.94443662204
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': 1.9444366220433447, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 1.94)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', 'left', None)
0.00859733917094
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 1.08652876592
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.0865287659160812, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left', None)
Agent drove right instead of left. (rewarded 1.09)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None, None)
1.95527049582
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: 2.83609900453
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.836099004527557, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 2.84)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'right', 'left', 'left')
New state created!
0.0
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: right, reward: 0.920616048193
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left', 'left'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.920616048193344, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left', 'left')
Agent drove right instead of forward. (rewarded 0.92)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left', 'forward')
0.0
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 2.88807068404
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', 'forward'), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.8880706840415744, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.89)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'left', None)
1.75955136588
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 1.95217165053
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.9521716505265836, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 1.95)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None, None)
1.42667293924
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 1.6445039276
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.6445039275977635, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.64)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None, None)
1.53558843342
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 1.09073473575
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.0907347357457886, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.09)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', 'right', None)
0.812411131304
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: right, reward: 1.0517768325
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right', None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.0517768325022012, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right', None)
Agent drove right instead of left. (rewarded 1.05)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', None, None)
1.58513075301
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: right, reward: 1.73635688634
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.7363568863356686, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.74)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', None, None)
1.52891870773
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: right, reward: 1.54830425778
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.5483042577781405, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.55)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, 'forward', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: right, reward: -20.7053539676
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward', 'forward'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': -20.705353967592014, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.71)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None, None)
1.1420375162
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 2.21985918958
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 11, 't': 14, 'action': None, 'reward': 2.2198591895789423, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.22)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: right, reward: 0.414943626315
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.41494362631454496, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right', None)
Agent drove right instead of forward. (rewarded 0.41)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'forward', None)
0.525256775076
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: right, reward: 0.916795655989
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 0.9167956559887057, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', None)
Agent drove right instead of left. (rewarded 0.92)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'forward', 'forward')
0.0
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: -40.0238562911
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward', 'forward'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': -40.023856291053036, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.02)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'forward', 'right')
New state created!
0.0
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: right, reward: 1.00432162661
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', 'right'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.0043216266106827, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', 'right')
Agent followed the waypoint right. (rewarded 1.00)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None, None)
1.00890021954
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: left, reward: 1.12077371368
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': 1.1207737136751086, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.12)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None, None)
0.634679406291
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 1.13148487868
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.1314848786799705, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded 1.13)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', 'left', None)
0.0320268883767
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 1.08024143599
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left', None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 1.0802414359920278, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left', None)
Agent drove right instead of left. (rewarded 1.08)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', None, None, 'right')
0.14585412502
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: -0.374200271402
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, 'right'), 'deadline': 3, 't': 22, 'action': 'right', 'reward': -0.3742002714016941, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'right')
Agent drove right instead of forward. (rewarded -0.37)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', 'left', None, None)
0.838315798492
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 0.937159429385
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 0.9371594293853271, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 0.94)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, 'left', None)
0.778802885458
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: -0.767175382385
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', None), 'deadline': 1, 't': 24, 'action': 'left', 'reward': -0.7671753823851664, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, 'left', None)
Agent drove left instead of forward. (rewarded -0.77)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 1
\-------------------------
Environment.reset(): Trial set up with start = (4, 6), destination = (6, 3), deadline = 25
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward', 'forward')
0.830033500883
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: 2.18398008428
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', 'forward'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 2.183980084282429, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', 'forward')
Agent followed the waypoint left. (rewarded 2.18)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 2.6952548846
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.6952548846043394, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.70)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'left', None)
1.8558615082
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 2.75332354411
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.753323544105587, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 2.75)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 1.06500665265
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.0650066526546291, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.07)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None, 'forward')
0.0
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: -40.5978889778
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None, 'forward'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -40.59788897775543, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.60)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None, None)
1.88298022313
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 1.07993855642
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.0799385564172048, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent properly idled at a red light. (rewarded 1.08)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', None, None)
0.542182692193
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: forward, reward: 1.25370091771
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.2537009177093772, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove forward instead of left. (rewarded 1.25)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None, 'left')
0.994861171122
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 1.6514619783
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'left'), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.6514619783019364, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'left')
Agent drove right instead of left. (rewarded 1.65)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'forward', None)
0.721026215533
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 1.05810762677
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.0581076267672849, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', None)
Agent drove right instead of left. (rewarded 1.06)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None, None)
1.7000563899
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 2.68419974471
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 2.6841997447096775, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 2.68)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None, 'right')
New state created!
0.0
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 1.04600331164
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None, 'right'), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.0460033116356873, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, 'right')
Agent drove right instead of forward. (rewarded 1.05)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'forward', None, 'left')
0.532995320626
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 1.29621242925
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, 'left'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.2962124292545547, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, 'left')
Agent drove right instead of left. (rewarded 1.30)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'forward', None)
0.595106170314
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 1.12759871149
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.1275987114866397, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', None)
Agent drove right instead of left. (rewarded 1.13)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, 'left', None)
0.884157699645
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.7107506031
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', None), 'deadline': 12, 't': 13, 'action': None, 'reward': 1.7107506031036621, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 1.71)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None, None)
1.7000563899
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 1.54042437599
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.5404243759907212, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.54)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'left', 'forward')
0.56638426206
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 1.57927121167
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', 'forward'), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 1.5792712116669942, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', 'forward')
Agent drove right instead of forward. (rewarded 1.58)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'right', None)
1.91785928041
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.87233043733
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.8723304373326566, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right', None)
Agent properly idled at a red light. (rewarded 1.87)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', 'left', None)
0.696721880109
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: left, reward: 2.14549378738
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left', None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 2.145493787381771, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left', None)
Agent followed the waypoint left. (rewarded 2.15)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, None, 'forward')
0.440835130826
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: left, reward: 0.878269720394
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'forward'), 'deadline': 7, 't': 18, 'action': 'left', 'reward': 0.8782697203940901, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent drove left instead of forward. (rewarded 0.88)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, 'left', None)
0.884157699645
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 2.15402604498
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', None), 'deadline': 6, 't': 19, 'action': None, 'reward': 2.154026044981151, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 2.15)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'forward', None, None)
1.60548102525
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: 1.05188333403
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None, None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.0518833340337348, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, None)
Agent followed the waypoint right. (rewarded 1.05)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, 'forward', 'forward')
0.0
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: None, reward: 0.865839056343
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', 'forward'), 'deadline': 4, 't': 21, 'action': None, 'reward': 0.8658390563434779, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', 'forward')
Agent properly idled at a red light. (rewarded 0.87)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', 'right', 'left', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: -19.8832564453
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'right', 'left', 'forward'), 'deadline': 3, 't': 22, 'action': 'right', 'reward': -19.883256445289266, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.88)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', 'left', None, None)
1.53861148275
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 1.33916689036
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.3391668903584142, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.34)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, 'left', 'forward')
0.56638426206
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: right, reward: 0.955594526266
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', 'forward'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.9555945262664556, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, 'left', 'forward')
Agent drove right instead of forward. (rewarded 0.96)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 2
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (6, 3), deadline = 25
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None, None)
0.883082142486
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.65498744968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.654987449676435, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded 1.65)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None, 'left')
0.501658120599
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: 0.732086062387
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'left'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 0.7320860623866159, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'left')
Agent drove forward instead of left. (rewarded 0.73)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', 'forward', 'left')
New state created!
0.0
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: right, reward: 0.119813681431
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward', 'left'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.11981368143060922, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward', 'left')
Agent drove right instead of left. (rewarded 0.12)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward', 'right')
New state created!
0.0
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 1.60137875926
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward', 'right'), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.6013787592600874, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward', 'right')
Agent drove right instead of forward. (rewarded 1.60)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None, None)
2.05088711981
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 1.07255085018
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.072550850181052, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 1.07)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None, 'left')
0.956421743315
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: -0.00326529092517
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'left'), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -0.0032652909251734696, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'left')
Agent drove left instead of forward. (rewarded -0.00)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None, None)
1.66074381967
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 1.54276451746
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.5427645174619866, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.54)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None, None)
1.68094835289
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 2.35836341268
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.358363412681985, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.36)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None, 'forward')
0.0
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 1.06733873696
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.0673387369602008, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'forward')
Agent properly idled at a red light. (rewarded 1.07)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None, None)
1.68094835289
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 1.51193300795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.5119330079479416, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.51)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None, None)
2.39568475017
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: 2.44003559171
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.440035591714228, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 2.44)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: left, reward: 1.55077575773
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 1.5507757577265302, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.55)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None, 'left')
1.31707797819
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 1.69565067081
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, 'left'), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.695650670807697, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 1.70)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None, 'left')
0.956421743315
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: left, reward: 0.925068875815
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'left'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 0.9250688758150358, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'left')
Agent drove left instead of forward. (rewarded 0.93)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: right, reward: 2.04181826742
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward', None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 2.0418182674220224, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward', None)
Agent followed the waypoint right. (rewarded 2.04)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'right', 'forward', 'left')
New state created!
0.0
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: right, reward: 2.19319612076
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward', 'left'), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 2.193196120756131, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward', 'left')
Agent followed the waypoint right. (rewarded 2.19)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, 'forward', None)
0.595106170314
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: right, reward: 1.07181419451
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 1.0718141945135002, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', None)
Agent drove right instead of left. (rewarded 1.07)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, None, None)
1.7000563899
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: right, reward: 1.67298032021
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 1.672980320208184, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.67)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', None, None)
1.60548102525
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: right, reward: 2.052439911
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None, None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 2.0524399109985225, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, None)
Agent followed the waypoint right. (rewarded 2.05)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'left', None, None)
1.53861148275
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: right, reward: 0.662097222292
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 0.6620972222923738, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 0.66)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, 'right', None)
1.91785928041
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: None, reward: 1.34712566701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.3471256670143013, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right', None)
Agent properly idled at a red light. (rewarded 1.35)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None, 'left')
0.501658120599
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: forward, reward: 0.403812867628
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'left'), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.4038128676275833, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'left')
Agent drove forward instead of left. (rewarded 0.40)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, 'forward', None)
0.595106170314
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: -0.661356707907
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': -0.6613567079066109, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', None)
Agent drove right instead of left. (rewarded -0.66)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'left', 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 1.58003884951
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward', None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.5800388495059694, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward', None)
Agent followed the waypoint right. (rewarded 1.58)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: left, reward: -0.0160240141961
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 1, 't': 24, 'action': 'left', 'reward': -0.016024014196123204, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded -0.02)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 3
\-------------------------
Environment.reset(): Trial set up with start = (5, 6), destination = (3, 3), deadline = 25
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', 'left', 'right')
New state created!
0.0
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 0.089391364949
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left', 'right'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.0893913649489666, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left', 'right')
Agent drove right instead of forward. (rewarded 0.09)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'right', 'left', 'left')
New state created!
0.0
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 0.0982721076055
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left', 'left'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.09827210760549798, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left', 'left')
Agent drove right instead of left. (rewarded 0.10)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'forward', 'left')
0.0
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: right, reward: 1.85279426316
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', 'left'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.8527942631564485, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', 'left')
Agent drove right instead of left. (rewarded 1.85)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None, None)
1.7000563899
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 1.41249679797
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.4124967979679317, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.41)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', 'forward', 'right')
New state created!
0.0
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 0.652664225393
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward', 'right'), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.6526642253925968, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward', 'right')
Agent drove right instead of forward. (rewarded 0.65)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None, None)
0.576836284443
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 0.196296861442
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.19629686144168457, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove right instead of left. (rewarded 0.20)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None, None)
2.05088711981
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: 2.67314587916
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 2.673145879161346, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 2.67)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None, 'forward')
1.32354093903
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 1.98212220377
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.9821222037741446, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'forward')
Agent properly idled at a red light. (rewarded 1.98)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 1.08908684348
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.0890868434790806, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.09)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'left', None)
1.84250785641
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: 1.24887712445
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 1.2488771244493657, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 1.25)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'left', 'left')
1.39858212198
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 2.66788958289
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', 'left'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.667889582887125, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.67)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', 'forward', 'left')
New state created!
0.0
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 1.63309453542
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward', 'left'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.6330945354190138, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward', 'left')
Agent drove right instead of forward. (rewarded 1.63)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None, None)
0.887737613939
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: right, reward: 0.121180058505
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 0.12118005850512747, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 0.12)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: left, reward: 1.02116592202
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 1.0211659220203222, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.02)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, 'forward', None)
0.721026215533
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: right, reward: 0.889720525848
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.889720525848316, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', None)
Agent drove right instead of left. (rewarded 0.89)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', None, 'forward')
New state created!
0.0
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: right, reward: -20.6846482457
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', None, 'forward'), 'deadline': 10, 't': 15, 'action': 'right', 'reward': -20.684648245678613, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.68)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', 'forward', None, None)
0.57483599122
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: right, reward: 1.5093273457
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 1.5093273457001986, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None, None)
Agent followed the waypoint right. (rewarded 1.51)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', None, None)
0.57483599122
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: right, reward: 2.50018600459
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None, None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 2.500186004587119, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None, None)
Agent followed the waypoint right. (rewarded 2.50)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', None, None)
0.35350229591
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 1.42177755459
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.421777554593038, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent drove right instead of forward. (rewarded 1.42)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'forward', None, None)
0.542182692193
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: 0.437368225607
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': 0.43736822560722566, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove forward instead of left. (rewarded 0.44)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'left', None, None)
0.887737613939
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: 1.26021812674
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.2602181267351389, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 1.26)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, 'left', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: -20.3202209694
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'left', 'forward'), 'deadline': 4, 't': 21, 'action': 'right', 'reward': -20.320220969438868, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.32)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, 'left', 'right')
1.09459861673
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 1.19973899042
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', 'right'), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 1.1997389904195368, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', 'right')
Agent followed the waypoint right. (rewarded 1.20)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'left', None, None)
1.66074381967
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: right, reward: 1.11131656051
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.111316560507325, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.11)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'forward', 'forward', 'left')
1.01241258937
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: 1.10882019214
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward', 'left'), 'deadline': 1, 't': 24, 'action': None, 'reward': 1.1088201921423464, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'forward', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.11)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 4
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (1, 5), deadline = 20
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.36074772638
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.360747726382457, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward', None)
Agent drove right instead of forward. (rewarded 1.36)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'right', None, 'forward')
0.762700519915
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 1.39702796532
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.397027965322692, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None, 'forward')
Agent drove right instead of left. (rewarded 1.40)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'forward', None, None)
0.596839378711
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 0.794726975334
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 0.7947269753341119, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None, None)
Agent drove right instead of forward. (rewarded 0.79)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', 'left', None)
0.696721880109
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: left, reward: 1.99003034796
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.9900303479601331, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left', None)
Agent followed the waypoint left. (rewarded 1.99)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'right', 'forward', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: 0.0236379088108
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward', 'forward'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.02363790881084815, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward', 'forward')
Agent drove right instead of forward. (rewarded 0.02)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward', None)
0.721026215533
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: right, reward: 1.41998759738
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.41998759738175, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', None)
Agent drove right instead of left. (rewarded 1.42)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None, None)
1.7000563899
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: right, reward: 1.4115353955
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.411535395495377, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.41)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, 'left', None)
0.884157699645
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: None, reward: 2.51769967051
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.517699670506609, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 2.52)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'left', None, None)
1.53861148275
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: right, reward: 2.48793551802
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 2.4879355180218568, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 2.49)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', 'right', 'left')
New state created!
0.0
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: -0.116764619017
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right', 'left'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': -0.11676461901703461, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right', 'left')
Agent drove right instead of forward. (rewarded -0.12)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None, None)
2.05088711981
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: left, reward: 1.08926565979
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 1.0892656597921586, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 1.09)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', None, None)
0.883082142486
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: right, reward: 1.28181029015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.2818102901463657, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, None)
Agent drove right instead of forward. (rewarded 1.28)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 1.70440456025
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.7044045602533884, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.70)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 0.947908156144
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.9479081561437077, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.95)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 1.22843905175
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.2284390517474535, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.23)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 0.95620409911
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.956204099109899, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.96)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None, 'left')
0.501658120599
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: forward, reward: 0.567196027615
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'left'), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.5671960276147108, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'left')
Agent drove forward instead of left. (rewarded 0.57)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', 'right', None)
0.932093981903
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 0.467240188929
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.4672401889287848, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right', None)
Agent drove right instead of left. (rewarded 0.47)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'forward', None, 'right')
0.519040709133
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 0.5718498172
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, 'right'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.5718498172001705, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, 'right')
Agent drove right instead of left. (rewarded 0.57)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'left', None, None)
1.53861148275
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 0.625338424719
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 0.6253384247189784, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 0.63)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 5
\-------------------------
Environment.reset(): Trial set up with start = (3, 4), destination = (7, 5), deadline = 25
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'left', 'forward')
0.0
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: right, reward: -19.0007415894
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'left', 'left', 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': -19.000741589432483, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.00)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', 'left', None)
0.0
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: right, reward: 1.12797109002
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.127971090019404, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left', None)
Agent followed the waypoint right. (rewarded 1.13)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None, None)
1.53861148275
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: right, reward: 1.04979428694
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.0497942869402286, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None, None)
Agent followed the waypoint right. (rewarded 1.05)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'left', 'left')
New state created!
0.0
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 1.83162335193
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left', 'left'), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.8316233519344498, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', 'left')
Agent drove right instead of forward. (rewarded 1.83)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'forward', None, None)
0.542182692193
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: forward, reward: 0.816689528697
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 0.8166895286967865, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, None)
Agent drove forward instead of left. (rewarded 0.82)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'forward', 'forward')
0.830033500883
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: 2.32909149149
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', 'forward'), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 2.3290914914907956, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', 'forward')
Agent followed the waypoint left. (rewarded 2.33)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None, None)
1.68094835289
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.01438649582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.0143864958152582, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.01)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None, None)
2.39568475017
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 1.21999288978
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.2199928897792522, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 1.22)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.50396998452
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward', None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.5039699845162655, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward', None)
Agent drove right instead of forward. (rewarded 1.50)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', 'left', 'left')
New state created!
0.0
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 0.377608737466
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left', 'left'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.37760873746636214, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left', 'left')
Agent drove right instead of left. (rewarded 0.38)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None, None)
2.05088711981
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: 2.49932943298
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 2.4993294329784983, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 2.50)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None, 'left')
0.501658120599
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: forward, reward: 0.18563181268
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'left'), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 0.18563181267992068, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'left')
Agent drove forward instead of left. (rewarded 0.19)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None, 'forward')
1.18328710598
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: 0.369770662351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'forward'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 0.3697706623506962, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'forward')
Agent drove forward instead of left. (rewarded 0.37)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: None, reward: 0.797771169847
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': 0.7977711698473335, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.80)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None, None)
0.887737613939
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: right, reward: 1.2040676221
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.2040676220974085, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 1.20)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None, None)
1.7000563899
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: right, reward: 1.69235675488
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 1.692356754878803, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.69)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', 'left', None, None)
1.66074381967
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: 2.15002576195
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 2.15002576194755, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, None)
Agent followed the waypoint right. (rewarded 2.15)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, 'forward', 'left')
New state created!
0.0
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: right, reward: 0.0127922056851
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward', 'left'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 0.01279220568512085, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward', 'left')
Agent drove right instead of forward. (rewarded 0.01)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', 'left', None, None)
0.576836284443
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: right, reward: 0.594471319254
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None, None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 0.5944713192543418, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None, None)
Agent drove right instead of left. (rewarded 0.59)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, 'left', None)
0.884157699645
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: None, reward: 1.69488773916
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', None), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.694887739155707, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 1.69)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', None, None, None)
1.7000563899
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: right, reward: 1.0021187911
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.0021187910955094, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.00)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, None, 'right')
1.35695588568
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: 1.10736678279
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'right'), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 1.1073667827908023, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'right')
Agent followed the waypoint right. (rewarded 1.11)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, 'left', None)
0.0058137515366
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: -0.44814149319
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': -0.44814149318989616, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', None)
Agent drove left instead of forward. (rewarded -0.45)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'left', None, 'forward')
0.777693998645
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 0.471721056875
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, 'forward'), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 0.4717210568748278, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None, 'forward')
Agent followed the waypoint right. (rewarded 0.47)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'right', None, None)
1.08445901546
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: -0.457454149315
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None, None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': -0.45745414931459694, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'right', None, None)
Agent drove right instead of forward. (rewarded -0.46)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 6
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (2, 7), deadline = 30
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', None, None)
0.57483599122
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: right, reward: 2.06380243388
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None, None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 2.0638024338840264, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None, None)
Agent followed the waypoint right. (rewarded 2.06)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None, 'forward')
0.440835130826
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: left, reward: 1.20651064023
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'forward'), 'deadline': 29, 't': 1, 'action': 'left', 'reward': 1.2065106402256731, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent drove left instead of forward. (rewarded 1.21)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None, 'left')
1.84202197184
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 1.10137826456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.1013782645584147, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 1.10)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, 'right', None)
1.12218298493
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 1.91782800446
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.9178280044626455, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right', None)
Agent properly idled at a red light. (rewarded 1.92)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: left, reward: 1.55210255624
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': 1.552102556240773, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.55)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, 'forward', None)
1.35009694213
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 0.963838287625
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward', None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 0.9638382876254461, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward', None)
Agent followed the waypoint right. (rewarded 0.96)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'right', None, None)
New state created!
0.0
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 2.72467197808
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None, None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 2.7246719780772253, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None, None)
Agent followed the waypoint right. (rewarded 2.72)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None, 'left')
New state created!
0.0
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 1.0654845004
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None, 'left'), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 1.0654845003955389, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None, 'left')
Agent drove right instead of forward. (rewarded 1.07)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', 'left', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: -19.7152228475
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'left', 'left', 'forward'), 'deadline': 22, 't': 8, 'action': 'right', 'reward': -19.715222847476813, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.72)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None, None)
0.887737613939
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 1.40060771555
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 1.4006077155546477, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 1.40)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None, 'left')
1.84202197184
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 2.2304986134
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.2304986134042837, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 2.23)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'left', 'left')
0.889903264722
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 1.46132232607
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', 'left'), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 1.4613223260663757, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', 'left')
Agent followed the waypoint right. (rewarded 1.46)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, 'left', 'forward')
1.13839581254
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 1.7078144423
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', 'forward'), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 1.7078144422979806, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.71)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'right', None, 'left')
New state created!
0.0
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 0.966104146358
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None, 'left'), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 0.9661041463584179, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None, 'left')
Agent drove right instead of forward. (rewarded 0.97)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 0.579372303507
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward', None), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 0.5793723035071863, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward', None)
Agent drove right instead of left. (rewarded 0.58)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'left', 'left')
0.994210984204
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 1.2269177593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', 'left'), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 1.2269177593001852, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', 'left')
Agent drove forward instead of right. (rewarded 1.23)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None, 'left')
1.84202197184
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 2.03756378094
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'left'), 'deadline': 14, 't': 16, 'action': None, 'reward': 2.037563780939217, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'left')
Agent properly idled at a red light. (rewarded 2.04)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, None, 'right')
1.35695588568
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: right, reward: 0.883790640343
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, 'right'), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 0.8837906403430267, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, 'right')
Agent followed the waypoint right. (rewarded 0.88)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: left, reward: 1.03277820914
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 12, 't': 18, 'action': 'left', 'reward': 1.0327782091369215, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.03)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, None, None)
2.39568475017
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: forward, reward: 2.44357711557
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 11, 't': 19, 'action': 'forward', 'reward': 2.443577115565758, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 2.44)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None, 'left')
New state created!
0.0
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: right, reward: 1.09013903688
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None, 'left'), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 1.0901390368849515, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None, 'left')
Agent drove right instead of forward. (rewarded 1.09)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', 'right', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 1.40588981654
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right', 'forward'), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 1.405889816538151, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right', 'forward')
Agent drove right instead of left. (rewarded 1.41)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', 'right', None, None)
0.0
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: right, reward: 2.3535110758
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None, None), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 2.353511075804106, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None, None)
Agent followed the waypoint right. (rewarded 2.35)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, 'left', None)
0.884157699645
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 0.864856508602
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left', None), 'deadline': 7, 't': 23, 'action': None, 'reward': 0.8648565086022317, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 0.86)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, None, None)
1.7000563899
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: right, reward: 1.96084484361
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 6, 't': 24, 'action': 'right', 'reward': 1.960844843605379, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.96)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'green', 'forward', None, None)
0.596839378711
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: right, reward: 0.891288863479
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None, None), 'deadline': 5, 't': 25, 'action': 'right', 'reward': 0.8912888634790894, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None, None)
Agent drove right instead of forward. (rewarded 0.89)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'red', None, None, 'forward')
1.32354093903
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 0.814024404764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'forward'), 'deadline': 4, 't': 26, 'action': None, 'reward': 0.8140244047642002, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'forward')
Agent properly idled at a red light. (rewarded 0.81)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'red', 'right', 'right', None)
New state created!
0.0
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 0.713202005503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'right', None), 'deadline': 3, 't': 27, 'action': 'right', 'reward': 0.7132020055032811, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'right', None)
Agent drove right instead of left. (rewarded 0.71)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'red', 'left', 'forward', 'right')
New state created!
0.0
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: right, reward: 0.975580894851
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward', 'right'), 'deadline': 2, 't': 28, 'action': 'right', 'reward': 0.975580894850617, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward', 'right')
Agent followed the waypoint right. (rewarded 0.98)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: -0.461155430458
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 1, 't': 29, 'action': 'left', 'reward': -0.4611554304579155, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded -0.46)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 7
\-------------------------
Environment.reset(): Trial set up with start = (4, 4), destination = (1, 5), deadline = 20
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'right', None, None)
1.029750805
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: right, reward: 2.16882044062
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.1688204406197933, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None, None)
Agent followed the waypoint right. (rewarded 2.17)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: 1.22891953332
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 1.2289195333226748, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.23)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'left', 'left', None)
0.322569431885
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 0.853072051737
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 0.8530720517371458, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left', None)
Agent drove right instead of forward. (rewarded 0.85)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.71092978938
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.7109297893782496, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.71)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'right', None, None)
0.75084011491
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 1.7438669866
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.7438669865990493, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None, None)
Agent drove right instead of left. (rewarded 1.74)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None, 'right')
New state created!
0.0
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 0.119911154713
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None, 'right'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.11991115471338176, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None, 'right')
Agent drove right instead of forward. (rewarded 0.12)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'right', 'left', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: -19.2400906203
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'right', 'left', 'forward'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': -19.24009062031324, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.24)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'left', None)
1.8558615082
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 2.21411434206
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.2141143420603657, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 2.21)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 1.70241903239
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.702419032388795, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.70)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 2.47924104641
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.479241046408703, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.48)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward', 'left')
0.0
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 0.547677632083
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward', 'left'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.5476776320834164, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward', 'left')
Agent drove right instead of left. (rewarded 0.55)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None, 'forward')
0.0
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: -39.4169855298
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -39.41698552976779, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.42)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None, None)
0.35350229591
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.64591281732
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.645912817316676, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent drove right instead of forward. (rewarded 1.65)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None, 'left')
0.994861171122
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: -0.286534682901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, 'left'), 'deadline': 7, 't': 13, 'action': 'right', 'reward': -0.28653468290079187, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, 'left')
Agent drove right instead of left. (rewarded -0.29)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None, 'forward')
0.440835130826
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: 1.15934298616
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'forward'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 1.159342986161369, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent drove left instead of forward. (rewarded 1.16)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'right', None, 'right')
1.22863816071
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 1.8380511835
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None, 'right'), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.8380511835030624, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None, 'right')
Agent properly idled at a red light. (rewarded 1.84)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'left', None)
0.291264188226
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: 1.23844227328
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 1.2384422732786338, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', None)
Agent drove left instead of right. (rewarded 1.24)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None, None)
2.39568475017
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 0.418094648909
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 0.41809464890924053, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 0.42)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'left', None)
0.0058137515366
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: left, reward: -0.118839946021
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left', None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -0.11883994602118064, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left', None)
Agent drove left instead of forward. (rewarded -0.12)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'left', None, 'forward')
0.777693998645
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: right, reward: 0.854591623006
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None, 'forward'), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 0.854591623006481, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'left', None, 'forward')
Agent followed the waypoint right. (rewarded 0.85)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 8
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (6, 7), deadline = 20
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, 'left', 'forward')
1.36359165186
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 2.98725002795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left', 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.9872500279456684, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.99)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'left', None)
0.67705501984
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 0.879751060593
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.87975106059325, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left', None)
Agent drove right instead of forward. (rewarded 0.88)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None, None)
1.88298022313
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 1.29934603803
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.2993460380294966, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent properly idled at a red light. (rewarded 1.30)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None, None)
1.88298022313
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 1.10327050817
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.1032705081719294, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent properly idled at a red light. (rewarded 1.10)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', 'left', None)
0.960967974172
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 1.0094305468
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.0094305468047726, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left', None)
Agent properly idled at a red light. (rewarded 1.01)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None, None)
1.88298022313
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 0.941950487646
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 0.9419504876464637, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent properly idled at a red light. (rewarded 0.94)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', None, 'right')
New state created!
0.0
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 1.87777356581
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None, 'right'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.8777735658118568, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None, 'right')
Agent drove right instead of left. (rewarded 1.88)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None, 'left')
0.956421743315
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: left, reward: 0.873522950922
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'left'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 0.8735229509220734, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'left')
Agent drove left instead of forward. (rewarded 0.87)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: left, reward: 1.59685118639
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 1.5968511863911377, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 1.60)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None, None)
2.20372388794
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: forward, reward: 1.10668734505
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.106687345045702, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None, None)
Agent followed the waypoint forward. (rewarded 1.11)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None, None)
2.39568475017
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: 0.958400218167
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 0.9584002181673437, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 0.96)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None, None)
2.39568475017
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: forward, reward: 1.16961369553
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.1696136955289633, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 1.17)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'left', None)
1.84250785641
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: 2.46054987349
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 2.46054987349083, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 2.46)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', 'left', None)
New state created!
0.0
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: -0.304609409702
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': -0.3046094097023647, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left', None)
Agent drove right instead of forward. (rewarded -0.30)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', None, 'right')
New state created!
0.0
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 0.184705044942
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None, 'right'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.1847050449419282, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None, 'right')
Agent drove right instead of left. (rewarded 0.18)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'left', None)
0.291264188226
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 1.36793153531
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 1.3679315353057948, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left', None)
Agent drove left instead of right. (rewarded 1.37)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None, 'left')
0.501658120599
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: -0.197763270274
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'left'), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -0.1977632702736558, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'left')
Agent drove forward instead of left. (rewarded -0.20)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.14251388901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 2.142513889013114, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.14)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None, None)
0.887737613939
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: 1.03385633007
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.033856330070438, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None, None)
Agent drove right instead of left. (rewarded 1.03)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, 'forward', None)
1.62668313799
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 0.168380505645
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward', None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 0.16838050564479268, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'forward', None)
Agent followed the waypoint right. (rewarded 0.17)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 9
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (2, 3), deadline = 25
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', None, None)
0.35350229591
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 0.0919439690852
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.0919439690852264, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent drove right instead of forward. (rewarded 0.09)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'left', None)
1.84250785641
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: 1.60257947112
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 1.6025794711244892, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 1.60)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None, None)
1.68094835289
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.60303254492
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.6030325449157627, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.60)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'forward', None)
0.758431571131
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 0.296533456248
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.29653345624781924, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward', None)
Agent drove right instead of forward. (rewarded 0.30)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 2.74259344989
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.742593449887087, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 2.74)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 1.17768159855
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.1776815985539537, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.18)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 1.59055318966
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.590553189661694, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.59)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None, 'right')
New state created!
0.0
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: right, reward: 1.65367847631
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'right'), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.6536784763112993, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'right')
Agent drove right instead of left. (rewarded 1.65)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left', None)
1.84250785641
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: left, reward: 2.75600451316
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 2.75600451316332, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 2.76)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'forward', None)
0.595106170314
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 0.259457523526
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.2594575235261287, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward', None)
Agent drove right instead of left. (rewarded 0.26)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'left', 'forward')
0.1618253345
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 0.49578073885
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', 'forward'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 0.49578073885022145, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', 'forward')
Agent drove forward instead of left. (rewarded 0.50)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None, None)
2.39568475017
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: forward, reward: 2.74372426673
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 2.743724266728775, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 2.74)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None, None)
1.68094835289
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: None, reward: 0.862248803215
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 0.8622488032151294, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 0.86)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None, None)
2.39568475017
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: 1.357092096
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.3570920960018018, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 1.36)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'forward', 'forward')
0.91076205642
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 0.847988582708
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward', 'forward'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.8479885827078643, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward', 'forward')
Agent drove right instead of forward. (rewarded 0.85)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'left', None)
1.8558615082
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.17889600733
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.1788960073335684, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left', None)
Agent properly idled at a red light. (rewarded 1.18)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None, None)
1.31316158458
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.85689158357
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.8568915835678386, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None, None)
Agent properly idled at a red light. (rewarded 1.86)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None, None)
2.05088711981
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: 1.09315366582
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 1.0931536658159893, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 1.09)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', 'left', 'right', 'forward')
0.0692529098007
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 1.10608280915
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right', 'forward'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.1060828091482977, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right', 'forward')
Agent drove right instead of left. (rewarded 1.11)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'right', 'left', None)
New state created!
0.0
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: right, reward: 2.22787958648
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left', None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 2.2278795864758547, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left', None)
Agent followed the waypoint right. (rewarded 2.23)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', None, None, None)
1.7000563899
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 1.89495431061
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None, None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.8949543106063964, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None, None)
Agent followed the waypoint right. (rewarded 1.89)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: -0.105697452008
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': -0.10569745200829084, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded -0.11)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, None, None)
1.06483696661
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: 0.268798510307
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 0.2687985103074373, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None, None)
Agent drove left instead of right. (rewarded 0.27)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', 'forward', None, None)
1.88298022313
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 0.3570877665
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, None), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.3570877664998511, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None, None)
Agent properly idled at a red light. (rewarded 0.36)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'forward', None, 'right')
0.519040709133
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 0.60068864419
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None, 'right'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.6006886441900448, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'forward', None, 'right')
Agent drove right instead of left. (rewarded 0.60)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Testing trial 10
\-------------------------
Environment.reset(): Trial set up with start = (6, 2), destination = (1, 4), deadline = 25
0
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
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epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
Simulating trial. . .
epsilon = 0.0000; alpha = 0.0000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', 'forward', None)
New state created!
0.0
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: right, reward: 1.95845724954
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.958457249539978, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward', None)
Agent drove right instead of forward. (rewarded 1.96)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'right', 'left', None)
New state created!
0.0
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: right, reward: 0.764518852721
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.764518852720758, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left', None)
Agent drove right instead of left. (rewarded 0.76)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, None, 'forward')
1.18328710598
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: forward, reward: 0.583309904951
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, 'forward'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 0.5833099049508215, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, 'forward')
Agent drove forward instead of left. (rewarded 0.58)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None, None)
0.35350229591
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: right, reward: 1.42246568726
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None, None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.4224656872634585, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None, None)
Agent drove right instead of forward. (rewarded 1.42)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'left', None)
1.84250785641
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: left, reward: 1.95153094629
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.9515309462940724, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left', None)
Agent followed the waypoint left. (rewarded 1.95)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'forward', None)
0.0
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 0.836938105662
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.836938105661511, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward', None)
Agent drove right instead of forward. (rewarded 0.84)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None, None)
2.05088711981
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: 2.43258563757
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None, None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 2.432585637565152, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None, None)
Agent followed the waypoint left. (rewarded 2.43)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None, 'forward')
0.440835130826
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: left, reward: 0.400412610585
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, 'forward'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 0.40041261058549305, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, 'forward')
Agent drove left instead of forward. (rewarded 0.40)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None, None)
1.60548102525
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 1.46930178127
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None, None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.4693017812680982, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None, None)
Agent followed the waypoint right. (rewarded 1.47)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'right', None)
0.768416434781
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: left, reward: 1.68130179934
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right', None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 1.6813017993357033, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right', None)
Agent followed the waypoint left. (rewarded 1.68)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None, None)
2.39568475017
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: 2.76544424292
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.765444242916125, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None, None)
Agent followed the waypoint forward. (rewarded 2.77)
56% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
Simulation ended. . .
In [20]:
# Load the 'sim_default-learning' file from the default Q-Learning simulation
vs.plot_trials('sim_default-learning.csv')
Question 6¶
Using the visualization above that was produced from your default Q-Learning simulation, provide an analysis and make observations about the driving agent like in Question 3. Note that the simulation should have also produced the Q-table in a text file which can help you make observations about the agent's learning. Some additional things you could consider:
- Are there any observations that are similar between the basic driving agent and the default Q-Learning agent?
- Approximately how many training trials did the driving agent require before testing? Does that number make sense given the epsilon-tolerance?
- Is the decaying function you implemented for $\epsilon$ (the exploration factor) accurately represented in the parameters panel?
- As the number of training trials increased, did the number of bad actions decrease? Did the average reward increase?
- How does the safety and reliability rating compare to the initial driving agent?
Answer:
They are still getting into major accidents and taking bad actions. Both of their safety and reliability ratings are still F.
Epsilon decays by 0.05 each time. Since it starts from 1, there are 20 training trials. Yes, the decaying function is a linear function and it is accurately represented in the parameters.
Yes, as the number of training trials increased, the number of bad actions decreased. This can be seen by the black dotted lines in the first panel of the visualisation. The average reward compared to the initial driving agent is higher with all of them being above -3.5. Also as the number of trials increases it seems that the average reward is also increasing, though we would need more trials to verify this.
The safety and reliability ratings are still F because of major accidents and bad decisisons.
Improve the Q-Learning Driving Agent¶
The third step to creating an optimized Q-Learning agent is to perform the optimization! Now that the Q-Learning algorithm is implemented and the driving agent is successfully learning, it's necessary to tune settings and adjust learning paramaters so the driving agent learns both safety and efficiency. Typically this step will require a lot of trial and error, as some settings will invariably make the learning worse. One thing to keep in mind is the act of learning itself and the time that this takes: In theory, we could allow the agent to learn for an incredibly long amount of time; however, another goal of Q-Learning is to transition from experimenting with unlearned behavior to acting on learned behavior. For example, always allowing the agent to perform a random action during training (if $\epsilon = 1$ and never decays) will certainly make it learn, but never let it act. When improving on your Q-Learning implementation, consider the impliciations it creates and whether it is logistically sensible to make a particular adjustment.
Improved Q-Learning Simulation Results¶
To obtain results from the initial Q-Learning implementation, you will need to adjust the following flags and setup:
'enforce_deadline'- Set this toTrueto force the driving agent to capture whether it reaches the destination in time.'update_delay'- Set this to a small value (such as0.01) to reduce the time between steps in each trial.'log_metrics'- Set this toTrueto log the simluation results as a.csvfile and the Q-table as a.txtfile in/logs/.'learning'- Set this to'True'to tell the driving agent to use your Q-Learning implementation.'optimized'- Set this to'True'to tell the driving agent you are performing an optimized version of the Q-Learning implementation.
Additional flags that can be adjusted as part of optimizing the Q-Learning agent:
'n_test'- Set this to some positive number (previously 10) to perform that many testing trials.'alpha'- Set this to a real number between 0 - 1 to adjust the learning rate of the Q-Learning algorithm.'epsilon'- Set this to a real number between 0 - 1 to adjust the starting exploration factor of the Q-Learning algorithm.'tolerance'- set this to some small value larger than 0 (default was 0.05) to set the epsilon threshold for testing.
Furthermore, use a decaying function of your choice for $\epsilon$ (the exploration factor). Note that whichever function you use, it must decay to 'tolerance' at a reasonable rate. The Q-Learning agent will not begin testing until this occurs. Some example decaying functions (for $t$, the number of trials):
You may also use a decaying function for $\alpha$ (the learning rate) if you so choose, however this is typically less common. If you do so, be sure that it adheres to the inequality $0 \leq \alpha \leq 1$.
If you have difficulty getting your implementation to work, try setting the 'verbose' flag to True to help debug. Flags that have been set here should be returned to their default setting when debugging. It is important that you understand what each flag does and how it affects the simulation!
Once you have successfully completed the improved Q-Learning simulation, run the code cell below to visualize the results. Note that log files are overwritten when identical simulations are run, so be careful with what log file is being loaded!
In [5]:
!python smartcab/agent.py
/-------------------------
| Training trial 1
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (5, 6), deadline = 25
0.996207210863
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9962; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, None)
New state created!
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: right, reward: 1.79517856783
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.7951785678274381, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.80)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
New state created!
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: forward, reward: -10.6225535455
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -10.622553545518645, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.62)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', 'forward')
New state created!
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: forward, reward: -39.5286512607
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -39.52865126069748, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.53)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 1.40012555869
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.4001255586926498, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.40)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 0.911418972568
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.9114189725676732, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.91)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: 0.580794326807
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.5807943268069758, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.58)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: -20.5478718015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': -20.547871801524114, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.55)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.29732620751
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.2973262075077239, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.30)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: left, reward: -40.6518401256
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -40.651840125643425, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.65)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: right, reward: 1.67433581384
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.6743358138351774, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.67)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: -5.28487530168
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': -5.284875301683979, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.28)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'forward')
New state created!
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: right, reward: 1.44261357085
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.4426135708467407, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 1.44)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -9.2233482846
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -9.223348284601242, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -9.22)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'left', None)
New state created!
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 1.26764193746
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 1.2676419374638639, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.27)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', 'left')
New state created!
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.39727597379
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.3972759737948859, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.40)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', 'forward')
New state created!
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: -40.2978127797
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -40.29781277966616, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.30)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
New state created!
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.48808952569
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.4880895256946607, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.49)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 1.6355808634
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.6355808634015208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.64)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'left')
New state created!
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.00177706342
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.0017770634179861, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 1.00)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
New state created!
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 1.47019582344
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.4701958234403745, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.47)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, 'left')
New state created!
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: 0.588386111158
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': 0.5883861111583482, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 0.59)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', None, None)
New state created!
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: 1.48414719487
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 4, 't': 21, 'action': None, 'reward': 1.4841471948672975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.48)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: 1.97689784962
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 22, 'action': None, 'reward': 1.9768978496193546, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.98)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'left', None)
New state created!
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: -10.9387683536
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -10.938768353578428, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.94)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: -9.85771514569
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 1, 't': 24, 'action': 'left', 'reward': -9.85771514569164, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -9.86)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 2
\-------------------------
Environment.reset(): Trial set up with start = (6, 3), destination = (2, 5), deadline = 30
0.992428806976
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9924; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', 'left')
New state created!
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: left, reward: -19.672280164
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 30, 't': 0, 'action': 'left', 'reward': -19.672280164048324, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.67)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: None, reward: -5.2206126452
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 29, 't': 1, 'action': None, 'reward': -5.22061264520236, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.22)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: 2.66854371711
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': 2.6685437171072186, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 2.67)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 1.44125621087
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 1.4412562108685631, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 1.44)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
New state created!
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: 1.60470880355
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 1.6047088035531383, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.60)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'left')
New state created!
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: -10.2860591905
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': -10.286059190487341, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.29)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: left, reward: -10.9197772131
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': -10.919777213060144, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.92)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'right', None)
New state created!
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 0.485505969948
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 0.48550596994787976, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.49)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
New state created!
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 1.85097001833
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.850970018325343, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.85)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'right', None)
New state created!
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: -5.32787701901
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 21, 't': 9, 'action': None, 'reward': -5.327877019012776, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.33)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: -39.8449927943
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': -39.84499279426566, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.84)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: -9.82459016675
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': -9.82459016675114, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.82)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', 'right')
New state created!
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 1.15504328767
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 1.1550432876674859, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent drove right instead of forward. (rewarded 1.16)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: -0.130208067446
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': -0.13020806744643965, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded -0.13)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: None, reward: 1.30910943778
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.3091094377805041, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.31)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', 'forward')
New state created!
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: left, reward: 2.72179346082
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 15, 't': 15, 'action': 'left', 'reward': 2.721793460819391, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.72)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 1.77857323223
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.778573232234425, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.78)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 1.3928607049
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 17, 'action': None, 'reward': 1.3928607049029866, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.39)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: forward, reward: -9.04999580599
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': -9.049995805990848, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.05)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', None)
New state created!
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: left, reward: 0.90936389923
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 19, 'action': 'left', 'reward': 0.909363899229551, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.91)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 0.429157162243
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 0.4291571622431769, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.43)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None)
New state created!
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: -0.314413826728
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': -0.31441382672802576, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded -0.31)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, 'forward')
New state created!
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.03088355761
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 8, 't': 22, 'action': 'forward', 'reward': 1.0308835576146613, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 1.03)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 1.3440854747
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 23, 'action': 'right', 'reward': 1.3440854746992916, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.34)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: -9.59608700032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 24, 'action': 'left', 'reward': -9.596087000320555, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.60)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: forward, reward: 0.0598360120242
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 5, 't': 25, 'action': 'forward', 'reward': 0.059836012024168106, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.06)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: left, reward: -10.8411819688
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 4, 't': 26, 'action': 'left', 'reward': -10.841181968760486, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.84)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'green', 'forward', None)
New state created!
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: left, reward: -19.7487049734
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 3, 't': 27, 'action': 'left', 'reward': -19.748704973368245, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.75)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: None, reward: -4.76223003139
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 2, 't': 28, 'action': None, 'reward': -4.762230031394799, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.76)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('left', 'red', None, 'left')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: None, reward: 0.232563864228
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 1, 't': 29, 'action': None, 'reward': 0.2325638642277159, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.23)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 3
\-------------------------
Environment.reset(): Trial set up with start = (5, 3), destination = (7, 5), deadline = 20
0.988664733778
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9887; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'right')
New state created!
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: forward, reward: -10.6028445556
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -10.60284455556333, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -10.60)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: left, reward: -10.8607228659
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.860722865947693, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.86)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: forward, reward: -9.00440417009
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -9.004404170088296, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.00)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: forward, reward: -9.65990305131
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -9.65990305130901, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.66)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: None, reward: 1.32956497874
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.3295649787444437, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.33)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: None, reward: 1.78364434936
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.7836443493555743, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.78)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: forward, reward: 0.835413452684
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 0.8354134526835482, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.84)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: left, reward: 1.14279585097
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.1427958509693517, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.14)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: right, reward: 0.0663518348176
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 0.06635183481764051, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.07)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 1.27591135559
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.275911355590552, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.28)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'right', None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: right, reward: 1.47767922066
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.4776792206632603, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.48)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'right', 'left')
New state created!
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: left, reward: -19.3459255563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -19.345925556344746, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.35)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, 'left')
New state created!
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: forward, reward: 0.393028527716
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.39302852771562957, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded 0.39)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', None)
New state created!
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: None, reward: -5.167089295
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': None, 'reward': -5.167089295000681, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.17)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 4), heading: (0, -1), action: right, reward: 1.08151684878
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.0815168487835651, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.08)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'left', None)
New state created!
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -0.0509975716919
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -0.050997571691892896, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded -0.05)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: left, reward: 1.31217795968
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 1.3121779596778542, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.31)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
New state created!
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: forward, reward: -0.434251535317
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -0.43425153531721905, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded -0.43)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: right, reward: 2.13278283125
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 2.132782831250778, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.13)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: forward, reward: 0.948694043238
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': 0.9486940432382165, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.95)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 4
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (6, 5), deadline = 20
0.984914936916
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9849; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 1.07053206128
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.070532061283745, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.07)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 1.37312344533
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.3731234453314616, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.37)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
New state created!
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.59203506137
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.5920350613700955, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.59)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
New state created!
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.96792989468
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.967929894676764, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.97)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 1.63960706959
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.6396070695898897, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.64)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: -9.29426472196
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -9.294264721956187, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.29)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 2.76631282716
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 2.766312827160377, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.77)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: 0.496568427185
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 0.49656842718495464, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.50)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: -5.79936719982
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': -5.7993671998210035, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.80)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: forward, reward: -40.5871742583
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': -40.58717425833127, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.59)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'forward')
New state created!
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: forward, reward: -39.2985821839
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -39.29858218390017, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.30)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 1.7893182186
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.7893182186010623, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.79)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
New state created!
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: right, reward: 1.60405337599
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.6040533759877758, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.60)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'right')
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 0.653092513286
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.6530925132860408, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 0.65)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 2.02353764569
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.023537645693027, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.02)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: -4.21144747739
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': -4.2114474773894335, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.21)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: left, reward: -20.649492514
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -20.64949251398554, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.65)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 0.818815390539
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 0.8188153905389739, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.82)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: left, reward: -10.8229781445
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -10.822978144461587, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.82)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: right, reward: 0.890538672401
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 0.8905386724013108, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.89)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 5
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (4, 4), deadline = 20
0.981179362243
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9812; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'left')
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 0.365455505363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 0.3654555053625874, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded 0.37)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: None, reward: 1.80861759916
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.808617599155538, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.81)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', 'right')
New state created!
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 1.88322794414
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.8832279441418207, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.88)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 1.87648377341
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.87648377341305, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.88)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: -10.2580453385
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -10.258045338460837, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.26)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 0.631450225256
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 0.6314502252564507, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 0.63)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: left, reward: 1.85438362171
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 1.8543836217119192, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.85)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: forward, reward: -9.92225012046
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': -9.922250120460339, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.92)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: forward, reward: -10.3752022711
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -10.375202271081065, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.38)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: left, reward: -39.5982221586
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -39.598222158557185, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.60)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'left')
New state created!
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: right, reward: 1.39893558291
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.3989355829126098, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.40)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'right')
New state created!
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 2.67377556956
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.6737755695592798, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.67)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'right', None)
New state created!
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: left, reward: -40.9730224028
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -40.97302240282478, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.97)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'right', None)
New state created!
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: left, reward: -19.0610156792
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -19.061015679240008, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.06)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: -5.31933294678
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': None, 'reward': -5.319332946781177, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.32)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: -4.58594299974
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 5, 't': 15, 'action': None, 'reward': -4.585942999735822, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.59)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, 'left')
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: right, reward: 1.43549281714
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.43549281714218, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.44)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: 1.49658849597
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 1.496588495969792, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.50)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
1.35948572353
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 0.887012113161
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.8870121131610484, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.89)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 0.531438670804
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.5314386708038688, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.53)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 6
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (3, 4), deadline = 25
0.977457955817
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9775; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -10.035177475
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -10.035177475007469, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.04)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'right')
New state created!
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: -9.31663510482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -9.316635104815925, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -9.32)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -9.38293482923
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -9.382934829230322, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.38)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: -9.23705428901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -9.237054289012494, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.24)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.07714424794
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.0771442479353555, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.08)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: 1.1390078584
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.1390078583952215, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.14)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: forward, reward: -40.1647673434
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': -40.164767343424316, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.16)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: left, reward: -9.30249230934
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -9.302492309339552, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.30)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 1.75686902149
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.756869021492882, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.76)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: left, reward: -9.90355977447
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -9.903559774469677, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.90)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 2.34421036628
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 2.3442103662840434, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.34)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: -19.6270293468
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': -19.627029346807713, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.63)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: -9.19040856345
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': -9.190408563448319, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.19)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 1.6260109307
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': None, 'reward': 1.6260109307006754, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.63)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 0.546587224008
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 14, 'action': None, 'reward': 0.5465872240082038, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.55)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: -9.42066136238
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': -9.420661362379533, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.42)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'right', None)
New state created!
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: left, reward: -40.7567853676
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -40.75678536762804, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.76)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', 'left')
New state created!
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 1.72168630851
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.721686308511297, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.72)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, 'forward')
New state created!
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: -39.0996207575
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -39.09962075752279, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.10)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: -10.9568408559
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': -10.956840855907597, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.96)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: left, reward: -9.46455596201
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 5, 't': 20, 'action': 'left', 'reward': -9.464555962010783, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.46)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: forward, reward: 0.677257611948
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.6772576119480967, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.68)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: 1.07089352197
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 1.070893521965337, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.07)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 0.453149800332
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 0.4531498003321295, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.45)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: -9.23604205152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 24, 'action': 'left', 'reward': -9.236042051518885, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.24)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 7
\-------------------------
Environment.reset(): Trial set up with start = (8, 3), destination = (1, 6), deadline = 20
0.9737506639
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9738; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: forward, reward: 0.597893247336
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 0.5978932473359764, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.60)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: forward, reward: -9.97855194733
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -9.978551947329207, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.98)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: left, reward: -10.6020357419
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.602035741858261, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.60)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: forward, reward: -10.2207401704
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.220740170408183, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.22)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: left, reward: -10.8864762663
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -10.88647626625328, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.89)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'right', None)
New state created!
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: None, reward: 1.63954002498
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.6395400249830725, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.64)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', 'right')
New state created!
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: right, reward: 0.949271459029
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.9492714590293732, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove right instead of left. (rewarded 0.95)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: left, reward: 2.53176490651
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.531764906505261, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.53)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'right')
New state created!
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: 2.62658629347
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 2.626586293468822, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.63)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: -9.10150372489
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -9.101503724889136, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.10)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: 0.535932271568
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 0.5359322715683005, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.54)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: -39.5562657506
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -39.55626575060431, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.56)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: -10.683430179
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -10.683430178991388, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.68)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'forward', 'right')
New state created!
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: -9.82236940976
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -9.822369409757012, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent attempted driving left through a red light. (rewarded -9.82)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: -9.54737501185
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -9.547375011853958, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.55)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: -10.6734188275
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -10.673418827540594, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.67)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', 'left', None)
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: left, reward: 0.110785663999
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 0.11078566399867507, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded 0.11)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: left, reward: -40.275256919
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -40.27525691901046, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.28)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: -0.27069591509
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': -0.2706959150896937, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded -0.27)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: left, reward: -19.7270132836
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -19.72701328355615, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.73)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 8
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (6, 7), deadline = 25
0.97005743296
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9701; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'right', 'left')
New state created!
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: forward, reward: -9.00004543137
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': -9.000045431373211, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left')
Agent attempted driving forward through a red light. (rewarded -9.00)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
2.09505464222
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: right, reward: 1.60830946195
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.6083094619452476, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.61)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: forward, reward: -10.8693808613
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -10.869380861261673, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.87)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: right, reward: 0.724854611986
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.7248546119860182, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.72)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: right, reward: 0.380882139741
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.3808821397405019, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.38)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: forward, reward: 0.616544090495
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 0.616544090495, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.62)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: right, reward: 1.13016900765
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.1301690076522957, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.13)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 0.3285341909
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 0.3285341909000903, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.33)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: forward, reward: -10.7008089958
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -10.70080899577085, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.70)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: 0.23265800395
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.23265800395015201, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.23)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: forward, reward: 0.154521447968
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 0.15452144796781087, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.15)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: -9.47082777856
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -9.470827778557107, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.47)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: -10.0546661991
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -10.054666199090784, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.05)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 0.708892808192
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 0.7088928081922701, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.71)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 2.08626866308
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 2.086268663076961, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.09)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'right', 'left')
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 2.65767867587
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': 2.657678675868203, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 2.66)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: -9.70152128378
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -9.701521283783357, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.70)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: -9.9572219529
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': -9.957221952901397, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.96)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, 'left')
New state created!
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: -9.61161949323
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -9.611619493229629, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.61)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, 'left')
0.500888531709
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.32556454482
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.325564544820669, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 1.33)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', 'right', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 0.733335819767
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 0.7333358197671374, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.73)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', 'left', 'forward')
New state created!
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 1.23357753486
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 1.2335775348556028, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent drove forward instead of right. (rewarded 1.23)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: left, reward: -9.7045273201
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': -9.704527320102432, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.70)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'forward', 'forward')
New state created!
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: left, reward: -20.4604138088
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -20.46041380882275, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.46)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: 1.12465608523
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 1.124656085228331, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.12)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 9
\-------------------------
Environment.reset(): Trial set up with start = (5, 6), destination = (7, 3), deadline = 25
0.966378209667
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9664; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: right, reward: 1.71391745835
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.7139174583505807, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.71)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: left, reward: 1.74176604609
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 1.7417660460876945, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.74)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 0.833256705346
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.8332567053463538, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.83)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: -9.2729538249
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -9.272953824898298, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.27)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: -10.403212229
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -10.403212228976443, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.40)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 0.355915785191
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 0.3559157851906932, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.36)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: forward, reward: 1.53170486996
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.531704869955877, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 1.53)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: forward, reward: 1.51261028184
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.5126102818408107, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.51)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: forward, reward: -10.9098069261
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -10.909806926087848, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.91)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: forward, reward: 1.83630657227
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.836306572267331, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.84)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'left', 'left')
New state created!
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 0.303134586429
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 15, 't': 10, 'action': None, 'reward': 0.30313458642945623, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 0.30)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'left')
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: -5.04642847707
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 14, 't': 11, 'action': None, 'reward': -5.046428477066824, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.05)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'left')
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: -4.15941989811
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': None, 'reward': -4.159419898106207, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.16)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 2.61608607512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.6160860751191928, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.62)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: left, reward: -9.93582100407
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -9.935821004068632, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.94)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'right', None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: right, reward: 0.648969162512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.6489691625117278, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 0.65)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: -4.44120795136
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': -4.44120795136321, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.44)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: right, reward: 0.892334305977
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 0.8923343059773547, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 0.89)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: forward, reward: -39.7266565057
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -39.7266565057033, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.73)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: forward, reward: 1.26453716754
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': 1.2645371675426529, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.26)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'green', 'left', 'left')
New state created!
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: -0.274104513405
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 5, 't': 20, 'action': None, 'reward': -0.2741045134045994, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded -0.27)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', 'left', 'left')
New state created!
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: -9.09171411016
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 4, 't': 21, 'action': 'left', 'reward': -9.091714110163018, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -9.09)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', 'left', 'left')
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.71851166354
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 3, 't': 22, 'action': None, 'reward': 1.718511663539181, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.72)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: -10.9761135805
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -10.976113580535294, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.98)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: right, reward: 1.95993318899
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 1.9599331889904512, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.96)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 10
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (6, 6), deadline = 30
0.962712940891
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9627; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 1.29389533468
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.2938953346817257, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.29)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
0.351187300252
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 0.419668792631
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 0.41966879263138657, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.42)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: -39.8096427964
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': 'left', 'reward': -39.8096427963998, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.81)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 0.223128697167
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 0.22312869716699868, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.22)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: 1.67622722184
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.676227221842275, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.68)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: -10.9016120434
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': -10.901612043383377, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.90)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 1.37109176884
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 1.3710917688399689, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.37)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'right', 'left')
New state created!
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: left, reward: -39.027253072
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', 'left'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': -39.027253071995176, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.03)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 1.40787123598
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.4078712359794494, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.41)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'forward')
New state created!
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: -20.2469078883
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 21, 't': 9, 'action': 'right', 'reward': -20.246907888289336, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.25)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: -4.1051122822
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 20, 't': 10, 'action': None, 'reward': -4.105112282202665, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.11)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: 1.70128788407
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 11, 'action': 'left', 'reward': 1.7012878840719163, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 1.70)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'forward', 'right')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 2.57286899664
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 18, 't': 12, 'action': None, 'reward': 2.572868996636662, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.57)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: -20.8603103592
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 17, 't': 13, 'action': 'left', 'reward': -20.860310359208853, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.86)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: -5.20127492129
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': -5.201274921293098, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.20)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: -4.75589810543
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 15, 't': 15, 'action': None, 'reward': -4.75589810543169, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.76)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.01019255662
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.010192556621338, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.01)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: -9.85149223644
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 13, 't': 17, 'action': 'left', 'reward': -9.851492236437716, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.85)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 2.57152278119
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 12, 't': 18, 'action': 'right', 'reward': 2.571522781191859, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.57)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 2.24415969924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.244159699244027, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.24)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: -10.7679149014
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 20, 'action': 'forward', 'reward': -10.76791490142068, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.77)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, 'forward')
1.08128149815
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 0.899060018013
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 0.8990600180131393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 0.90)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'red', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 0.442555478763
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 0.44255547876261425, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded 0.44)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', None, 'left')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: -0.184657189693
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 7, 't': 23, 'action': 'forward', 'reward': -0.18465718969265443, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded -0.18)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 0.659712546667
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 6, 't': 24, 'action': 'right', 'reward': 0.6597125466665861, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.66)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: -10.9119608556
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 25, 'action': 'left', 'reward': -10.911960855552111, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.91)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 0.219197528535
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 26, 'action': None, 'reward': 0.2191975285348704, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.22)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: -0.58435645542
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 27, 'action': 'forward', 'reward': -0.5843564554203107, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded -0.58)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: -10.0864589563
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 2, 't': 28, 'action': 'forward', 'reward': -10.086458956285941, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -10.09)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: 0.995974298084
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 1, 't': 29, 'action': 'left', 'reward': 0.9959742980844781, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.00)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 11
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (2, 5), deadline = 20
0.959061573707
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9591; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: None, reward: 2.65532498958
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.65532498957992, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.66)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'right')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: right, reward: 2.31604920866
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.3160492086590834, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent followed the waypoint right. (rewarded 2.32)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 2.56466271852
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 2.564662718517911, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.56)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: -10.8349060141
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.834906014076271, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.83)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: -9.7632443032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -9.76324430320013, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.76)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: 0.906628127926
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.9066281279262951, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.91)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: forward, reward: 1.12147459614
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.1214745961352548, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 1.12)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: -10.3059755514
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -10.305975551394072, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.31)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: -9.44333781749
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -9.443337817491226, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.44)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.26082357325
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.2608235732527344, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.26)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 1.15302984597
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.153029845974068, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.15)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 0.595735442404
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.5957354424038372, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent drove right instead of left. (rewarded 0.60)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, 'right')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: None, reward: 2.5705866648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.5705866648006195, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.57)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: None, reward: 1.62371333506
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.6237133350600808, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.62)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: -10.5964544615
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -10.596454461471561, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.60)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', 'right')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -10.4387953723
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -10.438795372250425, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent attempted driving left through a red light. (rewarded -10.44)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: forward, reward: 0.0563841563226
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.056384156322552004, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 0.06)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', None)
0.0281920781613
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: -0.454867114719
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -0.45486711471869923, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded -0.45)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -9.47548777571
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -9.475487775710816, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.48)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 0.753482054005
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.7534820540048386, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 0.75)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 12
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (5, 2), deadline = 20
0.955424055389
Simulating trial. . .
epsilon = 0.9554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9554; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 1.83260333129
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.8326033312866252, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.83)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 1.92137203789
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.9213720378906611, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.92)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 1.59029070159
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.5902907015923082, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.59)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', 'forward')
New state created!
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: 1.49506400011
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.4950640001147608, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent drove right instead of forward. (rewarded 1.50)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 0.761370482248
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 0.7613704822475472, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.76)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: 1.71855526558
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.7185552655792893, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.72)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: 1.52390229659
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.5239022965879199, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 1.52)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 0.982275412102
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 0.9822754121015214, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded 0.98)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 2.35703560981
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.3570356098149565, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.36)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 2.29793554755
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 2.2979355475505265, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.30)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 1.69377749205
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.6937774920456583, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.69)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'right')
New state created!
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 0.684822679724
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.6848226797236101, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent drove right instead of forward. (rewarded 0.68)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 0.915201108964
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.9152011089635257, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.92)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'right')
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: -0.256909037735
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 7, 't': 13, 'action': 'right', 'reward': -0.2569090377350004, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove right instead of left. (rewarded -0.26)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: -9.21332029248
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -9.213320292484731, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.21)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 2.30617515834
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 2.3061751583363304, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.31)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'left')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 2.1365567514
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 2.1365567514014967, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.14)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.36137734266
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.3613773426581757, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.36)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -39.5379182455
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': -39.53791824547058, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.54)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', None)
1.485016763
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.88167000016
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.8816700001588986, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.88)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 13
\-------------------------
Environment.reset(): Trial set up with start = (8, 5), destination = (5, 2), deadline = 30
0.951800333411
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9518; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'forward')
New state created!
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 0.0431555502976
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 0.04315555029762619, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent drove right instead of forward. (rewarded 0.04)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: -9.35347608169
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': -9.353476081687703, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.35)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: right, reward: 1.67607356997
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 1.6760735699672709, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.68)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'right', None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: -39.813291152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 27, 't': 3, 'action': 'left', 'reward': -39.81329115199133, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.81)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: 1.93957945062
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 1.9395794506162112, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.94)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: -5.6289522089
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': -5.628952208903691, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.63)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 2.0038129744
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 2.003812974395067, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.00)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: left, reward: -0.000277382070882
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': -0.0002773820708815711, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent drove left instead of forward. (rewarded -0.00)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: right, reward: 1.11379019051
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.1137901905078602, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.11)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: left, reward: -9.65775158256
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 21, 't': 9, 'action': 'left', 'reward': -9.657751582557681, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.66)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', None)
New state created!
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: 2.41703295171
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 2.417032951712078, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.42)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 2.09986362865
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 11, 'action': None, 'reward': 2.0998636286520727, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.10)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -39.1826231627
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': -39.18262316273657, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.18)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.05851825481
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 1.0585182548142422, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.06)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, 'right')
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: -10.6140473486
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': -10.614047348613084, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -10.61)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: left, reward: -39.8258696251
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -39.82586962514824, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.83)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: left, reward: -9.55651543422
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 14, 't': 16, 'action': 'left', 'reward': -9.55651543421552, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.56)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 0.87976062992
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 13, 't': 17, 'action': None, 'reward': 0.8797606299198402, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.88)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', 'left', 'right')
New state created!
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 0.688885806766
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 12, 't': 18, 'action': 'right', 'reward': 0.6888858067660211, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove right instead of left. (rewarded 0.69)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'left', 'forward')
New state created!
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: -40.4949488236
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 11, 't': 19, 'action': 'left', 'reward': -40.49494882357845, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.49)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'left', 'left')
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 1.06210901481
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 1.062109014812448, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.06)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 0.91719207041
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 9, 't': 21, 'action': None, 'reward': 0.9171920704095748, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.92)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: -0.474618858725
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 8, 't': 22, 'action': 'left', 'reward': -0.4746188587245439, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded -0.47)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 1.12185657087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 7, 't': 23, 'action': None, 'reward': 1.1218565708720063, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.12)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: -10.0148387951
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 24, 'action': 'left', 'reward': -10.014838795098177, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.01)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'green', 'left', 'right')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 0.650279018058
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 5, 't': 25, 'action': 'right', 'reward': 0.6502790180583433, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove right instead of left. (rewarded 0.65)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: -4.72935347272
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 26, 'action': None, 'reward': -4.729353472717175, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.73)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 0.737427378594
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 3, 't': 27, 'action': 'right', 'reward': 0.7374273785941683, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.74)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: -0.0746845355444
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 2, 't': 28, 'action': 'left', 'reward': -0.07468453554440091, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded -0.07)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: None, reward: 0.169694285167
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 1, 't': 29, 'action': None, 'reward': 0.1696942851665495, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.17)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 14
\-------------------------
Environment.reset(): Trial set up with start = (1, 3), destination = (6, 7), deadline = 25
0.948190355446
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9482; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'forward')
0.0
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: -40.1749620217
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': -40.1749620217428, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.17)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: -10.3099323601
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -10.309932360082165, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.31)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
1.64307087642
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 2.39699055605
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 2.396990556054832, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.40)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -10.9052356447
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': -10.905235644736045, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.91)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 2.34491363377
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.3449136337719554, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.34)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: 2.26949953545
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.269499535451656, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.27)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: -5.69320344173
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 19, 't': 6, 'action': None, 'reward': -5.69320344172643, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.69)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 0.999159355405
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': 0.9991593554048406, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.00)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', 'right')
New state created!
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: left, reward: -9.07635175891
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -9.076351758914985, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent attempted driving left through a red light. (rewarded -9.08)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: -10.2578760941
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': -10.257876094075138, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.26)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 1.95615753279
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.95615753278812, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.96)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: right, reward: 1.70288403745
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.702884037454542, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.70)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: forward, reward: -10.2612531345
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': -10.261253134542754, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving forward through a red light. (rewarded -10.26)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: left, reward: 0.577344769376
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 0.5773447693760785, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.58)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: -10.6312749446
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': -10.631274944643302, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.63)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 1.67212832808
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 1.6721283280835173, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 1.67)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 0.871882761605
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': 0.8718827616049292, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.87)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: 0.947652973604
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 0.9476529736042241, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.95)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 15
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (3, 6), deadline = 20
0.944594069367
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9446; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: 1.46720920856
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.4672092085575485, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.47)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: -10.7846600801
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.784660080105336, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.78)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 1.704602166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.7046021659975263, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.70)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: 0.788154661524
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 0.7881546615242822, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.79)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: 1.78354599738
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.7835459973828236, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove forward instead of left. (rewarded 1.78)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: -19.2878966596
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': -19.28789665963434, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.29)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: -9.72302499914
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -9.723024999141387, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.72)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: -10.2831340993
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': -10.283134099344478, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.28)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: -40.3983228766
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -40.398322876608255, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.40)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 1.61772088261
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.6177208826106686, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.62)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: -40.6686066839
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -40.66860668394784, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.67)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.87109389046
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.8710938904553203, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.87)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: left, reward: -9.37889307158
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -9.378893071579672, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.38)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 0.210585376197
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.21058537619736273, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 0.21)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'forward')
1.28233135926
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: 2.21644126724
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 2.2164412672362386, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.22)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.03328548536
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.033285485355334, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.03)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: forward, reward: 0.597138819212
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.5971388192117972, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 0.60)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: 1.98593163141
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.9859316314096647, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.99)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: right, reward: 0.384785056616
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.38478505661593165, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.38)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: left, reward: -9.23472368103
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -9.234723681030895, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.23)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 16
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (5, 4), deadline = 20
0.941011423242
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9410; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: forward, reward: -40.4304488921
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -40.430448892111826, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.43)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: left, reward: -10.5700286389
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.570028638893438, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -10.57)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: forward, reward: -9.68738831277
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -9.687388312765988, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -9.69)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: left, reward: -10.2613521344
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -10.261352134422006, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.26)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: left, reward: -9.02559100077
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -9.02559100076908, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.03)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: right, reward: 0.271251820925
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.27125182092489886, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.27)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: 1.03979216729
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.039792167285791, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.04)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'right', None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: forward, reward: 0.325634597612
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 0.3256345976123245, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove forward instead of left. (rewarded 0.33)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 0.952124622783
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 0.9521246227829302, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.95)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', 'forward')
New state created!
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: -19.8048283896
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': -19.80482838962269, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.80)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: -10.2896006638
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -10.289600663796364, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.29)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.60504489633
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.6050448963321267, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.61)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: -20.8982815532
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': -20.89828155315835, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.90)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: -0.152580165455
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': -0.15258016545502084, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded -0.15)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 0.438252681032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.4382526810316746, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.44)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: -39.9302779923
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -39.9302779922945, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.93)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: 1.20007922932
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.2000792293228297, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.20)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: 1.27990810015
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 1.2799081001543011, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.28)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 0.0884108761472
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.088410876147198, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent drove right instead of left. (rewarded 0.09)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: left, reward: -9.90002967625
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -9.900029676249392, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.90)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 17
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (4, 7), deadline = 30
0.937442365338
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9374; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: left, reward: 2.39729685298
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 2.397296852980043, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.40)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: left, reward: -40.7231166545
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 29, 't': 1, 'action': 'left', 'reward': -40.72311665452071, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.72)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'right')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: left, reward: -9.99202406797
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 28, 't': 2, 'action': 'left', 'reward': -9.992024067966625, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -9.99)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.75937055541
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.7593705554135044, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.76)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: 0.743333192336
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 0.7433331923355578, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.74)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: 0.510444500365
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 0.5104445003647112, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.51)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'left', 'left')
New state created!
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.18815389438
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.1881538943811734, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.19)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.51525978016
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 7, 'action': None, 'reward': 2.5152597801602976, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.52)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: -10.3222914899
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': -10.322291489925247, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.32)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
1.0962686662
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: left, reward: 1.9222214168
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 1.9222214168023362, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.92)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: -10.8686216697
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': -10.868621669746851, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.87)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 1.02179646193
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.0217964619285176, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.02)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'forward')
1.74938631325
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 1.47195947623
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 1.4719594762334909, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.47)
57% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 18
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (8, 6), deadline = 20
0.933886844119
Simulating trial. . .
epsilon = 0.9339; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9339; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.1132089714
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.1132089714047033, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.11)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: right, reward: 1.45976704391
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.459767043906525, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.46)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: left, reward: 2.24443738545
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': 2.2444373854465454, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.24)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: 0.251311697979
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 0.2513116979789215, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.25)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: -10.9574711866
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -10.957471186647835, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.96)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -40.0278440929
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -40.02784409292496, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.03)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', 'right')
New state created!
0.0
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: left, reward: 0.69210295485
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 0.6921029548496556, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent drove left instead of right. (rewarded 0.69)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', 'right')
New state created!
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: right, reward: 1.12161925733
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.1216192573286874, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent drove right instead of left. (rewarded 1.12)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'right')
New state created!
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: right, reward: 2.52821984832
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 2.5282198483226503, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 2.53)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: forward, reward: -0.100340247182
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': -0.10034024718159462, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded -0.10)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: left, reward: 1.09539666476
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 1.0953966647575908, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded 1.10)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: right, reward: 1.79867478027
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.7986747802717324, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.80)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: -10.3960174858
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -10.396017485765313, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.40)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: right, reward: 1.70698427475
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.7069842747547026, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.71)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: right, reward: 0.884180488014
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.8841804880137935, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.88)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'left')
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: forward, reward: -40.57804985
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -40.57804984997566, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.58)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: forward, reward: -40.4329350719
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -40.43293507191635, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.43)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: left, reward: 0.922220317528
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 0.9222203175278463, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.92)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: None, reward: -5.29995065281
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 2, 't': 18, 'action': None, 'reward': -5.299950652805759, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.30)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: left, reward: 0.689154110213
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 0.6891541102132251, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 0.69)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 19
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (2, 6), deadline = 25
0.930344808241
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
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epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9303; alpha = 0.5000
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/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: left, reward: -9.58934691617
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -9.589346916171074, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -9.59)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 1.45532412543
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.455324125434211, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.46)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'right')
0.577521643834
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 0.721138083394
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.7211380833941547, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent drove right instead of forward. (rewarded 0.72)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 0.420693473823
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.4206934738229098, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.42)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 1.7492134873
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.7492134873045062, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.75)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'right', None)
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 0.965926942186
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.9659269421864276, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 0.97)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 2.23315109386
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.2331510938622703, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.23)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 2.21571272371
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 2.2157127237072265, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.22)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 1.71784995243
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.7178499524341138, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.72)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: -39.9668636104
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': -39.96686361044824, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.97)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: -10.9413408022
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -10.941340802214825, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.94)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 1.28905856113
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.2890585611314305, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.29)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.50903880862
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.50903880861871, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.51)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: 1.09625085202
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 1.0962508520246879, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.10)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, 'left')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: -10.5386250457
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -10.538625045690445, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.54)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'right', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 1.16989429624
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 1.1698942962428855, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 1.17)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: -10.5852318751
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': -10.585231875124842, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.59)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'right')
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 1.26829757152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 1.2682975715185396, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent drove right instead of left. (rewarded 1.27)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'right', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: -9.51613593152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -9.51613593152203, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.52)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: -10.0945290902
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': -10.094529090200728, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.09)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'forward', 'right')
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: -9.52401871538
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': -9.524018715378809, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -9.52)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: forward, reward: 0.313117065669
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.31311706566855657, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 0.31)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: -5.25803415261
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 22, 'action': None, 'reward': -5.258034152608772, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.26)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: -4.63864739436
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 2, 't': 23, 'action': None, 'reward': -4.638647394363664, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.64)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: left, reward: 0.194664970758
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 24, 'action': 'left', 'reward': 0.19466497075757694, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.19)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 20
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (4, 7), deadline = 20
0.926816206559
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9268; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: left, reward: 0.298346082889
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 0.29834608288878917, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.30)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 1.81776401912
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.8177640191239546, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.82)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 2.08560005061
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.0856000506083565, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.09)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: left, reward: -9.15024786215
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -9.150247862154536, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.15)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: right, reward: 2.43821019175
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 2.4382101917496115, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.44)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: -10.8822521553
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -10.88225215532909, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.88)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 2.10314755347
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.103147553474342, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.10)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: left, reward: 0.275088745959
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 0.27508874595883914, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.28)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'left')
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: None, reward: -4.33023640875
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': -4.330236408751444, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.33)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, 'left')
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: None, reward: 1.91850988883
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.91850988882948, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.92)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: None, reward: 1.41237904645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.4123790464509351, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.41)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: right, reward: 2.12335856665
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.1233585666547983, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.12)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 0.993353150572
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.9933531505723976, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.99)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', 'right')
New state created!
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: left, reward: -19.9422600169
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', 'right'), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -19.942260016929147, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'right')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.94)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'right')
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 1.25968805868
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.2596880586836237, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.26)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: None, reward: -5.49375999536
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 5, 't': 15, 'action': None, 'reward': -5.493759995357548, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.49)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: None, reward: -5.99834413208
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 4, 't': 16, 'action': None, 'reward': -5.998344132084693, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -6.00)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: right, reward: 0.898869786
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.898869786000363, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent drove right instead of forward. (rewarded 0.90)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: right, reward: -19.2098580093
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': -19.209858009279888, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.21)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: left, reward: -10.7317960557
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -10.731796055748239, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.73)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 21
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (8, 4), deadline = 20
0.923300988119
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9233; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, 'right')
New state created!
0.0
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: forward, reward: -40.1408058849
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -40.14080588493795, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.14)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: left, reward: -9.15908418953
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -9.15908418953492, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -9.16)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: left, reward: -9.0806702664
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -9.080670266403812, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.08)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 1.3843160164
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.3843160163958277, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.38)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: forward, reward: -9.86207986738
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -9.862079867383281, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.86)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: forward, reward: -10.0824344616
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -10.082434461613197, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.08)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: 0.183853152063
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.18385315206318498, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 0.18)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'left')
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: left, reward: 2.30167127059
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.3016712705892584, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.30)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', 'left')
New state created!
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 0.922209861612
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.9222098616120233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent properly idled at a red light. (rewarded 0.92)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: left, reward: -19.9428720568
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -19.942872056809207, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.94)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: right, reward: 0.71880035182
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.7188003518203198, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.72)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: forward, reward: -39.5011134356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -39.501113435563845, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.50)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: right, reward: -19.9161882643
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': -19.91618826426471, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.92)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 1.64353034583
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.6435303458306365, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.64)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: left, reward: -9.97661217014
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -9.976612170140749, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.98)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 1.83712914216
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.8371291421575413, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.84)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: right, reward: 1.04642598699
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.0464259869920853, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.05)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: 2.29339996647
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 2.2933999664747833, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.29)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: left, reward: -10.4844930506
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -10.484493050622415, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.48)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: -9.45751993078
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': -9.45751993077597, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.46)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 22
\-------------------------
Environment.reset(): Trial set up with start = (3, 6), destination = (8, 5), deadline = 20
0.919799102162
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9198; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'forward')
New state created!
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: forward, reward: -40.5633051974
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -40.56330519739014, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.56)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', 'forward')
0.0
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: -19.1171747533
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': -19.11717475330825, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.12)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 1.04469055558
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.044690555582997, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.04)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 2.32992512605
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.3299251260507, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.33)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: -20.1298334126
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': -20.129833412562242, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.13)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: -4.33049757185
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': -4.33049757185155, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.33)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: forward, reward: 1.78102946725
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.7810294672481302, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.78)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 1.45745978362
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.4574597836178038, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.46)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -39.4174684716
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -39.41746847163483, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.42)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.29132261331
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.2913226133094042, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.29)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -10.5550136166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -10.555013616550015, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.56)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: -4.13535294576
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': None, 'reward': -4.135352945760986, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.14)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: -5.20888684392
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': -5.208886843922777, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.21)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: left, reward: 1.40366512001
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.4036651200122545, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.40)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'forward')
1.63545872328
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 2.18813332341
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 2.188133323413841, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.19)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: left, reward: 1.18546708242
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 1.1854670824235054, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 1.19)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 1.03908665549
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.03908665548683, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.04)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: left, reward: 2.31748837304
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 2.317488373036161, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.32)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', None)
1.25797207317
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: right, reward: 1.19397741976
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.1939774197614497, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.19)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: left, reward: 0.17581075847
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 0.17581075846981964, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.18)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 23
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (6, 6), deadline = 20
0.916310498119
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9163; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', 'forward')
New state created!
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: -40.1095252914
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -40.109525291395, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.11)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: -10.3614781808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.361478180787907, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.36)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 1.78792031442
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.7879203144154878, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.79)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: forward, reward: -9.78343494318
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -9.783434943180056, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.78)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: -9.5429309541
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -9.54293095410084, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.54)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'left')
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: forward, reward: 1.91113574195
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.91113574195113, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 1.91)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'left', 'forward')
New state created!
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: forward, reward: -40.0038176293
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -40.003817629302134, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.00)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 0.130158981573
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 0.13015898157327332, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent drove right instead of left. (rewarded 0.13)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 0.101550037627
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 0.10155003762727433, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.10)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: forward, reward: 1.51982043001
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.5198204300071443, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 1.52)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'right', 'right')
New state created!
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: 1.07599389527
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.075993895270682, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent drove right instead of forward. (rewarded 1.08)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'right', 'forward')
New state created!
0.0
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: -5.32956396074
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 9, 't': 11, 'action': None, 'reward': -5.329563960735975, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.33)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: forward, reward: 0.292758783647
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.29275878364700314, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.29)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'right', None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 0.124882196228
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.12488219622844243, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 0.12)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 1.09559435911
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.0955943591097963, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.10)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 1.48904535611
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.489045356109578, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.49)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.1823461582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.1823461581956394, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.18)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: -0.645085032318
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -0.6450850323181024, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded -0.65)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: -19.1688817397
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -19.16888173968426, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.17)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: -20.1865324519
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 1, 't': 19, 'action': 'right', 'reward': -20.18653245190685, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.19)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 24
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (2, 6), deadline = 20
0.912835125616
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9128; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: -9.67379617031
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -9.67379617031162, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.67)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: -10.8597208125
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -10.85972081250796, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.86)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 0.521715699963
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 0.5217156999628981, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent drove right instead of forward. (rewarded 0.52)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'left')
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: 1.17892576573
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.1789257657349599, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 1.18)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: -10.2831877151
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.283187715114929, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.28)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: forward, reward: 0.175707534018
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 0.17570753401814554, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.18)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: forward, reward: 0.357181629317
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 0.35718162931691055, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.36)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 1.02604391366
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.0260439136583572, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.03)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 2.67705484755
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.677054847552137, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.68)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, 'left')
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: right, reward: 1.17520627349
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.1752062734911897, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded 1.18)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: left, reward: -39.034631177
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -39.03463117695432, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.03)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: right, reward: 1.66520565264
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.6652056526411283, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.67)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'forward', None)
1.06167928333
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: right, reward: 2.2282817048
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 2.228281704796187, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.23)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 0.839232494336
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.8392324943356062, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.84)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: 0.529746993954
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 0.529746993954317, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.53)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'forward', 'right')
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: -4.48333073953
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', 'right'), 'deadline': 5, 't': 15, 'action': None, 'reward': -4.483330739529872, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.48)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'left', 'left')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.65688369451
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.6568836945107717, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.66)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: -19.7505294708
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -19.750529470811784, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.75)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 0.34202018011
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 0.34202018010999446, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.34)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 0.531572590178
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.5315725901776014, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 0.53)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 25
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (3, 5), deadline = 20
0.909372934468
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9094; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: forward, reward: -39.6674057554
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -39.66740575539909, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.67)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: forward, reward: -10.8883030959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -10.888303095929714, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.89)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
1.4547433385
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 1.40670950737
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.406709507372798, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.41)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 2.62500039609
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.6250003960855373, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.63)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 1.35460881408
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.3546088140801205, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.35)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: -4.12374599152
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': -4.1237459915203445, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.12)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: forward, reward: 1.26482284183
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.2648228418301566, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.26)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.69150821578
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 2.82624547247
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.8262454724699473, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.83)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'right', None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: forward, reward: -9.07987020469
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.07987020469096, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.08)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: forward, reward: -10.4997927583
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': -10.499792758296834, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.50)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: -0.228354448051
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': -0.22835444805055471, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded -0.23)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'right', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: -9.48083314848
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -9.480833148483665, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.48)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
1.58189090458
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.64045333239
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.6404533323852084, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.64)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 2.51650777618
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 2.516507776178686, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.52)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
1.53224886485
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.31948044872
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.3194804487222906, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.32)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: -40.3234736275
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -40.32347362754961, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.32)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: -9.88806438905
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -9.888064389047695, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.89)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 2.13716002199
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 2.137160021988741, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 2.14)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: 0.0860395276927
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': 0.08603952769270151, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.09)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: -9.89620436981
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': -9.896204369812951, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.90)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 26
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (7, 2), deadline = 30
0.905923874681
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9059; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 2.11199028922
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 30, 't': 0, 'action': None, 'reward': 2.1119902892229305, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.11)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 1.41484498808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.4148449880813474, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.41)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: left, reward: -10.4425185362
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': 'left', 'reward': -10.442518536188505, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.44)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: forward, reward: -10.3728938765
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': -10.372893876535153, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.37)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'left', None)
2.02003071624
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 1.02425231484
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 1.0242523148405256, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.02)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: -10.7103903442
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': -10.710390344153579, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.71)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 0.656039030371
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 0.6560390303714211, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.66)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: left, reward: -39.6027056618
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': -39.60270566177311, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.60)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: left, reward: -9.30831913626
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': 'left', 'reward': -9.308319136262654, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.31)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: 0.389627250758
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 0.38962725075801674, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.39)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: -20.3482530779
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 20, 't': 10, 'action': 'right', 'reward': -20.348253077859592, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.35)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
2.25887684413
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 2.41673337472
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 11, 'action': None, 'reward': 2.4167333747216486, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.42)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: -9.5268512126
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': -9.526851212596963, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.53)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
1.13462292479
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 1.76589876249
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 1.7658987624851676, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.77)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, 'right')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.63700306266
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.6370030626588707, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.64)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.58031562243
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 15, 'action': None, 'reward': 2.580315622429291, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.58)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: left, reward: 2.619217905
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 2.6192179050016677, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.62)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: left, reward: -40.4704532056
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 13, 't': 17, 'action': 'left', 'reward': -40.47045320555818, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.47)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'right', None)
0.819770012492
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 2.44980729081
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 12, 't': 18, 'action': None, 'reward': 2.4498072908114787, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.45)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 0.702632328921
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 11, 't': 19, 'action': None, 'reward': 0.7026323289206955, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.70)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: left, reward: -10.9652506019
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 20, 'action': 'left', 'reward': -10.96525060190326, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.97)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 0.349929277419
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 0.3499292774190409, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.35)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 1.69327716878
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 1.6932771687779715, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.69)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: -10.1552023231
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 7, 't': 23, 'action': 'forward', 'reward': -10.15520232311478, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.16)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 0.975411611398
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 6, 't': 24, 'action': None, 'reward': 0.9754116113976836, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.98)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'green', None, 'left')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: -5.78340632855
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 5, 't': 25, 'action': None, 'reward': -5.7834063285521715, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.78)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: 1.1352744613
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 4, 't': 26, 'action': 'forward', 'reward': 1.1352744613006236, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 1.14)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'red', None, 'right')
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: None, reward: 0.459066838735
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 3, 't': 27, 'action': None, 'reward': 0.4590668387351149, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 0.46)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: right, reward: 2.06429329969
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 2, 't': 28, 'action': 'right', 'reward': 2.0642932996920234, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.06)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'red', None, 'left')
1.04932391192
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 1.81840499767
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 1, 't': 29, 'action': None, 'reward': 1.818404997667923, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.82)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 27
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (8, 4), deadline = 25
0.902487896451
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
Simulating trial. . .
epsilon = 0.9025; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: -20.0364168513
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': -20.036416851340437, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.04)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -39.0684070811
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -39.068407081137686, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.07)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.31027628093
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.3102762809293558, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.31)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.8454412089
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.845441208902879, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.85)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
2.50860689547
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 1.09870633652
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.0987063365202576, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.10)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -9.16850270073
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -9.168502700729816, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.17)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -19.10821597
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -19.10821596999665, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.11)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 1.09418230048
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.0941823004795534, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 1.09)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -19.7009390743
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -19.70093907429802, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.70)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.48539699977
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.485396999766326, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.49)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -9.06665167511
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -9.066651675107105, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.07)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: -9.42547141682
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': -9.42547141682471, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.43)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: -0.0773335729322
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': -0.07733357293223064, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent drove right instead of forward. (rewarded -0.08)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'left')
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: -4.48227589141
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 12, 't': 13, 'action': None, 'reward': -4.482275891406567, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.48)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: -0.108525947649
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': -0.1085259476492576, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent drove forward instead of left. (rewarded -0.11)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: None, reward: -5.48491204354
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': -5.484912043540393, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.48)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: -9.45327800572
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': -9.453278005721199, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.45)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: left, reward: -10.5797896501
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': -10.57978965011808, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.58)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: -10.6161204476
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -10.616120447609058, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.62)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: forward, reward: -0.214487349028
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': -0.21448734902843614, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded -0.21)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'left', None)
1.87602601103
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: 0.747676632959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 0.7476766329590101, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.75)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: right, reward: 0.015738235209
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 0.015738235208967777, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.02)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: 0.322932478057
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.3229324780566998, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 0.32)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: left, reward: 0.353325542151
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 23, 'action': 'left', 'reward': 0.3533255421508019, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.35)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: right, reward: 0.287509443955
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.2875094439545338, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 0.29)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 28
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (1, 4), deadline = 20
0.899064950161
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8991; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: forward, reward: -40.5623660577
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -40.56236605769798, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.56)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: forward, reward: -40.894663052
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -40.894663051985084, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.89)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 1.84767073206
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.8476707320618877, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.85)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: right, reward: 0.492536829176
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.4925368291763649, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.49)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: -9.08958412169
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -9.089584121692416, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.09)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 0.905640488933
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 0.9056404889330896, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.91)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: right, reward: 0.899389265007
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.8993892650066206, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.90)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: left, reward: 1.15115269779
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.151152697786313, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.15)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: left, reward: -0.0762972261842
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -0.07629722618418777, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.08)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: left, reward: -9.99585959124
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -9.995859591241942, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.00)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'left')
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 1.14344916399
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.143449163991626, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.14)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: right, reward: 0.270527685067
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.2705276850669365, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.27)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: 0.532480120394
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 0.5324801203938416, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.53)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, 'right')
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: forward, reward: -0.0873018099701
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -0.08730180997005721, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent drove forward instead of right. (rewarded -0.09)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: left, reward: -10.2052494877
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -10.205249487704165, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.21)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: left, reward: -9.47555194947
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -9.47555194946726, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.48)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: None, reward: 1.28978989811
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.289789898106848, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.29)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: forward, reward: -0.532492587614
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -0.532492587613805, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded -0.53)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
1.95521973052
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 0.871363671762
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.8713636717621989, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.87)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, 'right')
0.0
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -10.8404737192
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -10.84047371923569, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -10.84)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 29
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (5, 6), deadline = 30
0.895654986385
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8957; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: -39.7013981385
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': -39.70139813854333, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.70)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: -9.68606236538
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': -9.686062365384622, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.69)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: -10.2569091466
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': -10.256909146645308, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.26)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 1.39472168202
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 1.3947216820217463, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.39)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 2.47745371963
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 2.4774537196325226, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.48)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: 1.79810804442
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 5, 'action': 'left', 'reward': 1.7981080444224995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.80)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: -9.00115931501
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': -9.001159315011455, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.00)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', 'left')
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: forward, reward: 0.167245407492
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 0.16724540749222438, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded 0.17)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: left, reward: -9.50922279254
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 22, 't': 8, 'action': 'left', 'reward': -9.509222792540076, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.51)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: -5.05740474598
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': -5.057404745978672, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.06)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: forward, reward: 1.38106969773
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 1.3810696977336374, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 1.38)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: -20.8782668545
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 19, 't': 11, 'action': 'right', 'reward': -20.878266854545746, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.88)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: forward, reward: -9.01095457332
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': -9.010954573317287, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.01)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: None, reward: 2.70120792441
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 13, 'action': None, 'reward': 2.701207924405085, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.70)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: left, reward: -39.0734789903
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 16, 't': 14, 'action': 'left', 'reward': -39.073478990344995, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.07)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: forward, reward: -10.4661031698
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': -10.466103169840434, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.47)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: 0.788146390962
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 0.7881463909621313, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 0.79)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: -39.6443574858
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 13, 't': 17, 'action': 'left', 'reward': -39.64435748576984, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.64)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, 'right')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.06529831606
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 12, 't': 18, 'action': None, 'reward': 2.0652983160581195, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.07)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: -10.3043433526
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 19, 'action': 'left', 'reward': -10.304343352609902, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.30)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: -10.1190379968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 20, 'action': 'left', 'reward': -10.119037996821685, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.12)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: 0.489988684029
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 0.4899886840292266, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.49)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: None, reward: -4.38046989581
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 22, 'action': None, 'reward': -4.380469895811649, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.38)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 1.08109179506
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 7, 't': 23, 'action': 'right', 'reward': 1.0810917950584096, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 1.08)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: -0.220682574388
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 6, 't': 24, 'action': 'forward', 'reward': -0.22068257438750982, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent drove forward instead of left. (rewarded -0.22)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'red', None, 'left')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: -10.5036997523
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 5, 't': 25, 'action': 'left', 'reward': -10.503699752346952, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.50)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: -39.0196246965
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 4, 't': 26, 'action': 'left', 'reward': -39.01962469648457, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.02)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: -10.8125642035
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 3, 't': 27, 'action': 'left', 'reward': -10.81256420350682, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.81)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: 1.99971887148
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 2, 't': 28, 'action': 'left', 'reward': 1.9997188714795227, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.00)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 0.946887593397
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 1, 't': 29, 'action': 'right', 'reward': 0.946887593397048, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.95)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 30
\-------------------------
Environment.reset(): Trial set up with start = (3, 6), destination = (6, 7), deadline = 20
0.892257955882
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8923; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: None, reward: -4.50764911167
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': -4.50764911166737, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.51)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: 1.75670084604
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 1.756700846039193, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.76)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 2.82405809808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.8240580980836407, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.82)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: -10.8968263713
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -10.896826371266187, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.90)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 1.39531744193
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.3953174419275776, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.40)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -10.2837743645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': -10.283774364463332, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.28)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -19.9729078292
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -19.972907829197286, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.97)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: -4.68492863277
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 13, 't': 7, 'action': None, 'reward': -4.684928632768294, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.68)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'right', 'left')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 0.596606768143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 0.5966067681425365, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent drove right instead of forward. (rewarded 0.60)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', 'forward')
New state created!
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: 0.849676999126
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 0.8496769991256998, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 0.85)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'left')
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: 0.989581130073
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 10, 't': 10, 'action': None, 'reward': 0.989581130073057, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.99)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'right')
1.52353648785
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: 2.00489168269
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.0048916826873353, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.00)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: -9.77481248787
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -9.774812487865267, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.77)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'right', None)
0.796775961293
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 1.13348437203
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.1334843720277812, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.13)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: 0.692231132792
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.6922311327922631, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded 0.69)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: right, reward: 0.353634030214
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.3536340302140639, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 0.35)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: -5.87801641486
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 4, 't': 16, 'action': None, 'reward': -5.878016414858284, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.88)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: forward, reward: -10.0706486361
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -10.070648636139639, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.07)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: 1.48671868025
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.4867186802450676, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.49)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: -5.84301155998
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 1, 't': 19, 'action': None, 'reward': -5.8430115599769925, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.84)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 31
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (8, 4), deadline = 20
0.8888738096
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8889; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: left, reward: 1.76812498448
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.7681249844796671, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.77)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'forward')
1.91179602335
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: 1.17671793698
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.176717936977781, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.18)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: -5.49855164442
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': None, 'reward': -5.498551644421096, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.50)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: -5.30950112487
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': None, 'reward': -5.309501124874391, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.31)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: left, reward: 1.36254062228
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 1.3625406222811611, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.36)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
1.4016366313
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: 0.982877884361
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 0.982877884361091, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.98)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
1.42586465679
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: right, reward: 2.32636140509
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 2.3263614050867663, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.33)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: left, reward: -39.3001657834
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -39.300165783363504, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.30)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: None, reward: 1.10269949393
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.1026994939250527, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.10)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: left, reward: 0.16129994738
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 0.16129994738019193, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded 0.16)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, 'left')
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 1.49350032688
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.4935003268813671, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.49)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: left, reward: 0.620406730363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 0.6204067303630294, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.62)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 0.629857429158
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 0.6298574291584405, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.63)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: forward, reward: -9.97597043444
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -9.975970434444424, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.98)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', 'right')
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: 0.795311393461
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.7953113934607645, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 0.80)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: 1.40795486713
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 1.4079548671285433, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 1.41)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: -4.00159973986
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 4, 't': 16, 'action': None, 'reward': -4.001599739863218, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.00)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 0.759048651368
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 0.7590486513681738, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.76)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'right', None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: -10.5963228746
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': -10.596322874584793, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -10.60)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: -5.15142370343
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': -5.151423703434903, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.15)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 32
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (4, 7), deadline = 25
0.885502498671
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8855; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'left')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: forward, reward: -10.0146060056
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': -10.01460600561276, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.01)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: -40.6796780772
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -40.67967807717942, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.68)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
2.45906036593
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: 1.25985327409
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.2598532740948147, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.26)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: -9.93219158145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': -9.932191581451074, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.93)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: forward, reward: 0.718364329234
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 0.7183643292336318, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.72)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: left, reward: 2.75468986712
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 2.754689867122308, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.75)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 1.9347173504
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.9347173503959272, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.93)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 2.56573028778
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 2.565730287782129, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.57)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: None, reward: 1.28464165961
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.2846416596144017, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.28)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 1.34928047427
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.34928047427294, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent drove right instead of forward. (rewarded 1.35)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'forward')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 1.40628719393
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 1.4062871939255477, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.41)
56% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 33
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (4, 2), deadline = 25
0.882143974414
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8821; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: 1.88220273199
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.8822027319910517, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.88)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', 'right')
0.0
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: -10.2995958329
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -10.29959583294553, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -10.30)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: -19.8999091032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': -19.899909103151128, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.90)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: -10.7406742408
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -10.740674240842681, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.74)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 0.953809053585
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.9538090535854208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.95)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: 0.372157727699
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 0.37215772769880495, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove forward instead of left. (rewarded 0.37)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.33573325959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.3357332595917546, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.34)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: -9.1986823847
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -9.198682384698104, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -9.20)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: -10.0509143701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -10.050914370116907, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent attempted driving forward through a red light. (rewarded -10.05)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 2.36056463796
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.3605646379581326, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.36)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 2.32983636878
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.3298363687754247, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.33)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', 'left')
0.631965363195
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: 0.649394892197
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 0.6493948921974838, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove forward instead of left. (rewarded 0.65)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: -9.54976932535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -9.549769325345412, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.55)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: -20.4163285522
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': -20.416328552150407, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.42)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'right')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 1.59167163573
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.5916716357314509, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove right instead of left. (rewarded 1.59)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
1.80365661599
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.80184674702
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.8018467470164237, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.80)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: -5.7519845711
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': -5.751984571097369, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.75)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: left, reward: 1.18144305536
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 1.1814430553643944, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.18)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'forward', 'left')
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: left, reward: -19.5657925048
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 7, 't': 18, 'action': 'left', 'reward': -19.565792504775256, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.57)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: 2.27102186297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 6, 't': 19, 'action': None, 'reward': 2.2710218629739787, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.27)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: 2.39496952384
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 2.3949695238387094, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.39)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: forward, reward: 1.52839928653
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 1.5283992865258995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.53)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: -39.9025503285
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': -39.9025503285471, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.90)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', 'right', 'forward')
New state created!
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: -19.231293335
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -19.231293334993765, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.23)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: right, reward: 1.06883903417
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 1.068839034173052, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 1.07)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 34
\-------------------------
Environment.reset(): Trial set up with start = (5, 7), destination = (8, 4), deadline = 30
0.878798188331
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8788; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: left, reward: 1.77513672413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 1.7751367241260603, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.78)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 1.0426789242
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.0426789242004366, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.04)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: right, reward: 0.599809652324
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.5998096523240198, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.60)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.80275168151
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: None, reward: 1.64653356098
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.6465335609831828, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.65)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: 2.49248317083
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 2.4924831708259623, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.49)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: -5.00001645773
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': -5.000016457733608, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.00)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: 1.92058042347
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 1.9205804234711596, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.92)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: -10.726515558
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': -10.726515557950382, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.73)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: -10.7685997662
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 22, 't': 8, 'action': 'left', 'reward': -10.768599766172457, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.77)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: -9.25799123302
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': -9.257991233020945, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.26)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: -5.91362787322
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 20, 't': 10, 'action': None, 'reward': -5.913627873219044, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.91)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: 0.377354713862
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 0.37735471386224406, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.38)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: left, reward: -9.39993763361
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': -9.399937633612764, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.40)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: None, reward: 1.50237812632
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 17, 't': 13, 'action': None, 'reward': 1.5023781263214577, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.50)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: forward, reward: 1.58617450883
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': 1.586174508828309, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.59)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: left, reward: 0.792801985373
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'left', 'reward': 0.7928019853733036, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.79)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: None, reward: 1.49642965859
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.49642965858566, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.50)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: forward, reward: 0.912308368593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 0.9123083685934517, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.91)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 0.60911916941
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 12, 't': 18, 'action': 'right', 'reward': 0.6091191694100153, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 0.61)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.837652371
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 19, 'action': None, 'reward': 1.8376523710022197, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.84)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, 'right')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.33809408503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 10, 't': 20, 'action': None, 'reward': 2.338094085025742, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.34)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: 2.36270717045
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 21, 'action': 'left', 'reward': 2.362707170449673, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.36)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
1.87959911168
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 2.18416240976
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 22, 'action': 'forward', 'reward': 2.184162409758744, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.18)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: -10.9546628861
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 7, 't': 23, 'action': 'forward', 'reward': -10.95466288613794, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.95)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 0.426196491797
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 6, 't': 24, 'action': None, 'reward': 0.4261964917968315, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.43)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: -5.75950039026
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 5, 't': 25, 'action': None, 'reward': -5.759500390256116, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.76)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: 1.61731431882
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 26, 'action': 'forward', 'reward': 1.6173143188193908, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.62)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'green', 'right', 'left')
New state created!
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: 0.593340383951
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 3, 't': 27, 'action': 'forward', 'reward': 0.5933403839512892, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove forward instead of left. (rewarded 0.59)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 1.12593964068
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 2, 't': 28, 'action': 'right', 'reward': 1.1259396406802349, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.13)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: left, reward: -0.00579159544923
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 29, 'action': 'left', 'reward': -0.005791595449230025, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.01)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 35
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (2, 5), deadline = 25
0.875465092109
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8755; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'left')
New state created!
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: forward, reward: 0.856175761608
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 0.8561757616080401, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 0.86)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'forward')
0.0
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 2.22175617029
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.221756170290493, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.22)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 1.81160097238
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.8116009723823259, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.81)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 0.486340382257
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.4863403822566097, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.49)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 0.0166776845252
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 0.01667768452517182, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 0.02)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 0.947823757788
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.9478237577879617, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.95)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: left, reward: -9.94992850877
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -9.949928508766233, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.95)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: 1.28786357089
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.2878635708919213, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.29)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: right, reward: 1.18412979497
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.1841297949719607, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent drove right instead of left. (rewarded 1.18)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: right, reward: 0.463218830442
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.46321883044212253, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.46)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'right')
2.05115408515
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 1.32382185009
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.3238218500863137, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.32)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'left')
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: left, reward: 2.7306753347
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 2.730675334697812, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.73)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: 0.349386228702
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 0.3493862287015743, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.35)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: -19.1063028106
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': -19.10630281055395, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.11)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 1.74511660869
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.7451166086926113, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.75)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 1.14286693691
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.1428669369095443, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.14)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 0.912301798319
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 0.9123017983192502, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.91)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: -4.00566799488
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 17, 'action': None, 'reward': -4.005667994878317, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.01)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'left', 'right')
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: left, reward: 0.746929554351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 7, 't': 18, 'action': 'left', 'reward': 0.746929554351234, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent drove left instead of right. (rewarded 0.75)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'right', 'forward')
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: right, reward: -20.1904889003
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': -20.190488900344395, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.19)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: right, reward: 0.579484749875
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 0.5794847498751812, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.58)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'right', 'right')
New state created!
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 0.532619772698
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'right'), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 0.532619772698156, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'right')
Agent drove right instead of forward. (rewarded 0.53)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'red', 'right', None)
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: right, reward: 1.1122463096
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 1.1122463095978103, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 1.11)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, 'forward')
1.24275565456
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: None, reward: 0.613430106619
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.6134301066190764, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.61)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: -10.7618458362
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 24, 'action': 'left', 'reward': -10.761845836190535, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.76)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 36
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (3, 5), deadline = 30
0.872144637618
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8721; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.41744655886
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 2.40101081423
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 30, 't': 0, 'action': None, 'reward': 2.4010108142269253, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.40)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 1.72359278516
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 1.7235927851649369, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.72)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
1.14011343895
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: 1.14729835547
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 28, 't': 2, 'action': 'left', 'reward': 1.1472983554729659, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.15)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: 0.555401110838
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 27, 't': 3, 'action': 'left', 'reward': 0.5554011108378766, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded 0.56)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'left', None)
0.819803534795
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 0.994673976242
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 0.9946739762422696, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 0.99)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.98010014068
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.980100140684989, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.98)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: -4.4235110522
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 24, 't': 6, 'action': None, 'reward': -4.4235110521952885, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.42)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'right')
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: -5.30716488123
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 23, 't': 7, 'action': None, 'reward': -5.3071648812310235, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.31)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: -5.8121560996
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': -5.8121560995998935, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.81)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: forward, reward: 0.0971175994183
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 0.09711759941830711, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 0.10)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: right, reward: 1.67621370161
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 1.6762137016067113, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.68)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: right, reward: 0.736217064181
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 0.736217064180911, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.74)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.67198044924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 18, 't': 12, 'action': None, 'reward': 2.6719804492374255, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.67)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
1.64436012155
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 1.32614696906
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 13, 'action': None, 'reward': 1.3261469690562044, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.33)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, 'right')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 1.27859196872
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 1.278591968715003, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent drove right instead of left. (rewarded 1.28)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: forward, reward: 1.56402529209
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 1.5640252920875684, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.56)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: 0.36779597439
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 0.3677959743902752, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.37)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 0.823394976118
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 0.8233949761175816, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.82)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: left, reward: -9.16896581893
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 12, 't': 18, 'action': 'left', 'reward': -9.168965818929076, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.17)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'forward', 'right')
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 1.26382088245
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 11, 't': 19, 'action': 'right', 'reward': 1.2638208824460264, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent drove right instead of forward. (rewarded 1.26)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: forward, reward: 0.0571795807348
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 10, 't': 20, 'action': 'forward', 'reward': 0.0571795807348181, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.06)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: right, reward: 0.908340552618
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 0.9083405526179887, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.91)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, 'right')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: forward, reward: 0.916037627694
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 8, 't': 22, 'action': 'forward', 'reward': 0.9160376276937692, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent drove forward instead of right. (rewarded 0.92)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: left, reward: 1.20674411107
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 23, 'action': 'left', 'reward': 1.2067441110707726, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 1.21)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', 'right', None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: 0.794187784558
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 6, 't': 24, 'action': 'forward', 'reward': 0.7941877845576133, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent drove forward instead of right. (rewarded 0.79)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: -9.75739204749
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 25, 'action': 'forward', 'reward': -9.757392047485236, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.76)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: left, reward: -20.9069331991
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 4, 't': 26, 'action': 'left', 'reward': -20.906933199062955, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.91)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: -5.85462222785
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 27, 'action': None, 'reward': -5.854622227850381, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.85)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 0.467700956418
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 28, 'action': 'right', 'reward': 0.4677009564181529, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.47)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'red', None, None)
1.72464262124
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 0.172984902676
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 29, 'action': None, 'reward': 0.17298490267585942, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.17)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 37
\-------------------------
Environment.reset(): Trial set up with start = (4, 4), destination = (1, 7), deadline = 30
0.868836776911
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
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epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8688; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'left')
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: right, reward: 0.637896664122
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 0.6378966641224173, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove right instead of left. (rewarded 0.64)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: -9.94465448861
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': -9.944654488612567, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.94)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: right, reward: 0.789944675991
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.789944675991416, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.79)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
1.5092450415
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: left, reward: 2.61979448967
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 27, 't': 3, 'action': 'left', 'reward': 2.6197944896699816, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.62)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: forward, reward: -9.41753362189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': -9.417533621889667, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.42)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: forward, reward: -9.4788460998
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': -9.47884609980136, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.48)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: left, reward: -10.8330287576
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': -10.833028757567096, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.83)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: 1.62241781352
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.6224178135208103, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.62)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 1.73083526343
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.7308352634327084, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 1.73)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: forward, reward: -9.44105678937
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': -9.441056789373162, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.44)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: 2.55462027362
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 20, 't': 10, 'action': 'left', 'reward': 2.554620273624322, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.55)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: -40.6531593032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 19, 't': 11, 'action': 'left', 'reward': -40.65315930317309, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.65)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 0.455693103738
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 0.4556931037382158, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.46)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: forward, reward: -10.2840873009
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': -10.284087300914077, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.28)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: forward, reward: -9.92106251339
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': -9.921062513386989, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.92)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
1.49032885342
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 1.87789384308
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.8778938430801393, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.88)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
2.06451976559
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: 0.827766860534
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 0.8277668605343529, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.83)
43% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 38
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (7, 5), deadline = 20
0.865541462222
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8655; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 1.33957676556
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.3395767655570856, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.34)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: right, reward: -0.000660801326604
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': -0.0006608013266037327, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded -0.00)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
1.90922868654
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: None, reward: 2.16131489259
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.161314892586435, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.16)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: left, reward: -9.61740938456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -9.617409384557948, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.62)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, 'left')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 2.2063812114
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 2.206381211400701, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.21)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: left, reward: 0.691619639187
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 0.6916196391869702, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.69)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: None, reward: 2.65157194908
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.6515719490795058, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.65)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 2.69807768797
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 2.698077687973544, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.70)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -10.5322153003
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -10.532215300312433, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -10.53)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 0.639542811404
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.6395428114040413, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.64)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: 1.21365059098
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 1.2136505909783373, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.21)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 2.26281596823
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.262815968234638, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.26)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.26638299243
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.2663829924340675, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.27)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: forward, reward: 1.87281145141
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.8728114514145107, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.87)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: left, reward: -20.7119860967
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -20.711986096730715, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.71)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -0.425292485914
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -0.42529248591426394, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded -0.43)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'left')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: -5.08275251202
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 4, 't': 16, 'action': None, 'reward': -5.082752512016757, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.08)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'right')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -9.72740078369
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -9.727400783689248, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -9.73)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 1.40916559842
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.40916559842198, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.41)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: forward, reward: 0.0320715390006
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': 0.03207153900060333, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 0.03)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 39
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (2, 2), deadline = 30
0.862258645967
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
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epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8623; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.69316999872
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.6931699987191409, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.69)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 0.994619001407
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 29, 't': 1, 'action': None, 'reward': 0.9946190014071965, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 0.99)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.42833264463
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.4283326446332127, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.43)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: -9.03351258902
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': 'left', 'reward': -9.03351258902094, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.03)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.59829035616
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.5982903561567183, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.60)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: -4.71778805072
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 25, 't': 5, 'action': None, 'reward': -4.7177880507164165, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.72)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: 1.23344511406
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 1.2334451140606402, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 1.23)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: -5.98308083872
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': None, 'reward': -5.983080838723105, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.98)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: right, reward: 0.905185541127
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 0.905185541127392, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.91)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: -5.64588936823
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': -5.64588936822569, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.65)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'right')
1.31329314673
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: left, reward: 1.75777309426
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 20, 't': 10, 'action': 'left', 'reward': 1.7577730942559073, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.76)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: 2.33239058665
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 2.332390586648594, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 2.33)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: 1.73177015921
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 18, 't': 12, 'action': 'left', 'reward': 1.7317701592062726, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 1.73)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: forward, reward: 0.584310631424
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 0.5843106314240201, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.58)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
2.03527178956
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 1.86106771884
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.8610677188440345, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.86)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.9481697542
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 2.27660557438
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 15, 'action': None, 'reward': 2.2766055743825806, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.28)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: 0.51814254266
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 0.5181425426603976, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 0.52)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: -9.17131553692
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 13, 't': 17, 'action': 'left', 'reward': -9.171315536923949, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -9.17)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.33735615751
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 12, 't': 18, 'action': None, 'reward': 1.3373561575075565, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.34)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.50256879882
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.5025687988213114, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.50)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: -9.30491045648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 10, 't': 20, 'action': 'forward', 'reward': -9.304910456483787, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -9.30)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 1.13962080051
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 1.139620800514828, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.14)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.47742342128
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 8, 't': 22, 'action': None, 'reward': 1.4774234212813302, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 1.48)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', None, 'left')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: -4.56946894934
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 7, 't': 23, 'action': None, 'reward': -4.5694689493423235, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.57)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'green', None, 'left')
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 0.976226254175
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 6, 't': 24, 'action': 'forward', 'reward': 0.9762262541745392, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 0.98)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'red', 'forward', None)
1.74050705217
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: 0.7060944611
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 5, 't': 25, 'action': None, 'reward': 0.7060944610999318, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.71)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: left, reward: -10.0101456342
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 4, 't': 26, 'action': 'left', 'reward': -10.010145634177194, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.01)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: -4.84820007787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 3, 't': 27, 'action': None, 'reward': -4.848200077873278, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.85)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: -5.83203049084
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 2, 't': 28, 'action': None, 'reward': -5.832030490839498, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.83)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: right, reward: -0.85086168418
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 29, 'action': 'right', 'reward': -0.8508616841796868, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.85)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 40
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (4, 7), deadline = 20
0.858988280741
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8590; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: forward, reward: 0.933267050706
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 0.9332670507056338, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.93)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'right', 'left')
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: -4.90832376756
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': -4.908323767557927, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.91)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'left')
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: 0.00603159174075
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 0.006031591740747744, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded 0.01)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 1.67939144384
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.67939144384381, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 1.68)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
2.15792749822
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: left, reward: 2.246323785
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 2.246323784999448, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.25)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: 0.715151832699
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 0.7151518326993029, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.72)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
1.44614331306
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: 1.92714929924
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 1.9271492992439436, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.93)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: -20.2998783853
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': -20.29987838526395, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.30)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -9.41526429664
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -9.415264296639346, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.42)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.28174940885
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.281749408845751, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.28)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 0.706834957534
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.7068349575343166, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.71)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: None, reward: 0.176911929099
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.17691192909944697, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.18)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: 0.295855583048
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.295855583047689, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove forward instead of left. (rewarded 0.30)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: forward, reward: -0.198274349057
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -0.1982743490571467, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove forward instead of left. (rewarded -0.20)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'right')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: -5.96326946743
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 6, 't': 14, 'action': None, 'reward': -5.963269467426383, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.96)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 1.10521922573
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.1052192257260944, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.11)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: -4.38429517455
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 16, 'action': None, 'reward': -4.3842951745524665, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.38)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, 'left')
1.99368173174
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: left, reward: 2.27216068512
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 2.2721606851236062, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.27)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: left, reward: 0.870081459199
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': 0.8700814591988233, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 0.87)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: -10.0699808955
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': -10.069980895542162, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.07)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 41
\-------------------------
Environment.reset(): Trial set up with start = (5, 2), destination = (7, 6), deadline = 20
0.855730319321
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8557; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: None, reward: -5.05434083469
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': -5.054340834693104, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.05)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
0.949034585092
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: left, reward: 0.62546343791
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 0.6254634379098246, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 0.63)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: 2.74942491893
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.749424918928632, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.75)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 0.642981737226
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.6429817372260588, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded 0.64)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: -10.6133899109
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -10.613389910893572, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.61)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'left')
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: right, reward: 1.40166883338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.4016688333773746, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded 1.40)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: -5.50696335538
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': None, 'reward': -5.506963355384207, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.51)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: left, reward: -9.64423843811
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -9.64423843811049, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.64)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: right, reward: -19.5493152335
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': -19.549315233524535, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.55)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 1.08170528842
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.0817052884180065, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.08)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: left, reward: -9.93870290805
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -9.938702908049105, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.94)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, 'right')
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: 1.3173024522
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.3173024521970804, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.32)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: left, reward: -9.01371886078
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -9.013718860780696, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.01)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: left, reward: -20.5829085728
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -20.58290857276762, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.58)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: forward, reward: 2.19039920574
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 2.1903992057442414, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.19)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: 1.37665277838
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.3766527783809774, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.38)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: left, reward: -10.0946762555
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -10.09467625546447, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.09)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, None)
2.19011551993
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 1.21771504896
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 1.2177150489618855, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.22)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'right')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: -4.2292310416
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 2, 't': 18, 'action': None, 'reward': -4.229231041596146, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.23)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, 'left')
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 1.15489437392
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 1.1548943739188768, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.15)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 42
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (7, 2), deadline = 25
0.852484714662
Simulating trial. . .
epsilon = 0.8525; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8525; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8525; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8525; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8525; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8525; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8525; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8525; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8525; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'left')
2.13292120843
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: 2.33198155323
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 2.3319815532256767, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.33)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: 2.73011182953
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 2.730111829528884, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.73)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.16516059174
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.1651605917447798, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.17)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: -10.7362824431
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -10.736282443134739, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.74)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.6698081523
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.669808152301942, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.67)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: forward, reward: -40.6228991044
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -40.622899104418174, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.62)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', None)
1.06007934986
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: 1.40982466341
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.4098246634055749, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.41)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.48525354531
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 2.2240387515
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.2240387515010633, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.22)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'right')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 2.06105896112
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.0610589611188344, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.06)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: right, reward: 0.899236351885
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.8992363518848727, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.90)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'forward', None)
2.21125554135
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: None, reward: 2.28204694397
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.282046943967246, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.28)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: None, reward: 1.69004174828
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.6900417482760848, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.69)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: right, reward: -19.5954721449
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': -19.59547214491518, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.60)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None)
0.994472021968
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 1.08796735715
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 1.0879673571479085, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.09)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: -0.184674946483
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -0.18467494648327254, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.18)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 0.235033228034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.23503322803419602, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.24)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: -9.38616690045
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -9.386166900453363, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -9.39)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: -9.06647576592
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': -9.066475765919932, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.07)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: forward, reward: -10.2271008217
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -10.22710082173831, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.23)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', 'forward')
1.40224566992
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: left, reward: 1.47158580713
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 6, 't': 19, 'action': 'left', 'reward': 1.4715858071299461, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.47)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: None, reward: -4.94962693979
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': -4.949626939792692, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.95)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', 'left', None)
1.80903036599
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: None, reward: 0.6973027274
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 4, 't': 21, 'action': None, 'reward': 0.6973027273996759, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.70)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', 'left', None)
1.25316654669
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: None, reward: 1.20308604277
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 3, 't': 22, 'action': None, 'reward': 1.203086042774815, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.20)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: right, reward: -0.479957104108
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': -0.4799571041081032, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded -0.48)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: right, reward: -0.395805980433
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': -0.39580598043342574, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded -0.40)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 43
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (8, 4), deadline = 20
0.849251419897
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8493; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'right', 'left')
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: forward, reward: -9.09743681587
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -9.097436815868301, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left')
Agent attempted driving forward through a red light. (rewarded -9.10)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 1.83979440784
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.8397944078443218, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.84)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 1.506531575
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.5065315750043038, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.51)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: forward, reward: -9.34844799003
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -9.348447990025381, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.35)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: right, reward: 1.75149774091
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.7514977409078736, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.75)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: -9.3263824457
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -9.326382445696753, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.33)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: -10.4467549263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -10.446754926311655, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.45)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: right, reward: 1.22130501855
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.221305018554943, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.22)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: None, reward: 1.06608301192
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.066083011920438, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.07)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: forward, reward: 0.981143872314
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 0.9811438723137369, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove forward instead of left. (rewarded 0.98)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: -4.25421624692
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': -4.2542162469229305, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.25)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: forward, reward: -40.0641385527
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -40.064138552749675, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.06)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
1.68411134825
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 2.21652919833
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.216529198334089, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.22)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: -19.9109313442
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 7, 't': 13, 'action': 'right', 'reward': -19.910931344151393, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.91)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: left, reward: -10.1055235504
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -10.105523550378356, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.11)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: forward, reward: -9.15216747855
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -9.152167478548778, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.15)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: left, reward: 1.21313852907
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 1.213138529065665, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.21)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: -9.44354812833
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -9.443548128328015, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.44)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'right', None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 1.42954658417
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.4295465841659796, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.43)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: -0.196465603224
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 1, 't': 19, 'action': None, 'reward': -0.1964656032242419, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded -0.20)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 44
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (7, 4), deadline = 20
0.846030388338
Simulating trial. . .
epsilon = 0.8460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8460; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: 0.734224182081
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 0.7342241820811661, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.73)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: right, reward: 1.64384251767
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.6438425176655227, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.64)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
1.46565355698
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 2.65701952724
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.6570195272401245, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.66)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'right', 'left')
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: 1.51097182187
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.5109718218746386, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent drove right instead of forward. (rewarded 1.51)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: forward, reward: 0.406840102979
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 0.4068401029789336, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.41)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'left')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 1.08587973916
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.0858797391615986, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded 1.09)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: left, reward: -19.1776366717
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -19.177636671683082, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.18)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: right, reward: 2.74733081926
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 2.7473308192642203, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.75)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: left, reward: -40.2932263848
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -40.29322638476241, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.29)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: left, reward: 0.347774043094
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 0.34777404309434634, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.35)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 1.60849160574
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.6084916057385832, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.61)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: 0.566717762652
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.5667177626518398, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.57)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'left', None)
1.62176463287
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: 0.690730092918
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.6907300929176199, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.69)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: right, reward: 1.05915331829
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.0591533182930934, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 1.06)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: None, reward: -4.62977038666
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': -4.629770386664224, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.63)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'right')
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: forward, reward: 0.858623600082
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 0.8586236000818372, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove forward instead of left. (rewarded 0.86)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'right')
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: right, reward: 0.139934917124
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 0.13993491712443595, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent drove right instead of left. (rewarded 0.14)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: -4.63311318707
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 3, 't': 17, 'action': None, 'reward': -4.6331131870718245, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.63)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 1.87741323689
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.8774132368898266, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 1.88)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: left, reward: -0.524954466634
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -0.5249544666338656, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.52)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 45
\-------------------------
Environment.reset(): Trial set up with start = (6, 3), destination = (3, 7), deadline = 25
0.842821573472
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8428; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', None)
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: -20.2886355757
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -20.288635575661566, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.29)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: left, reward: 1.27225605919
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 1.2722560591870342, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.27)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
1.72350767664
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: forward, reward: 2.13815869237
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 2.138158692374832, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.14)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.22812629473
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: 1.21670159703
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.2167015970277337, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.22)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: 2.28865939587
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.2886593958736956, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.29)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: 0.844705946852
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 0.8447059468523742, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.84)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: -5.58026135166
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 6, 'action': None, 'reward': -5.580261351664609, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.58)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: left, reward: 1.78547625297
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 1.7854762529661552, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent drove left instead of forward. (rewarded 1.79)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'right')
1.28960072181
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 1.72705059534
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.7270505953432354, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.73)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: -39.695930336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': -39.695930335962736, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.70)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', 'left')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -10.486816217
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -10.486816216968027, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -10.49)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -9.60818110611
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -9.608181106106109, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.61)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 1.41306778745
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.41306778745337, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.41)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: right, reward: 0.831266041372
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 0.8312660413724109, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 0.83)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: left, reward: -9.82848895132
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -9.828488951317192, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.83)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: right, reward: 0.158545266286
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.1585452662861102, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.16)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: left, reward: 0.678157128461
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 0.6781571284609023, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 0.68)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: left, reward: -9.30619169307
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': -9.30619169307029, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.31)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: left, reward: -10.8013060245
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 7, 't': 18, 'action': 'left', 'reward': -10.801306024450772, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -10.80)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: -0.510272554341
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': -0.5102725543405354, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded -0.51)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, 'right')
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: forward, reward: 0.508153494978
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': 0.5081534949775486, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove forward instead of left. (rewarded 0.51)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'right', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: -5.46620070412
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 4, 't': 21, 'action': None, 'reward': -5.466200704116691, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.47)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'right', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: -20.8158334489
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': -20.81583344887066, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.82)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 0.366917426208
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 0.36691742620808165, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.37)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, 'left')
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: -5.21369368778
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 1, 't': 24, 'action': None, 'reward': -5.213693687776805, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.21)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 46
\-------------------------
Environment.reset(): Trial set up with start = (6, 2), destination = (1, 7), deadline = 20
0.839624928964
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8396; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: left, reward: 0.943524591245
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 0.9435245912446986, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.94)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'forward', 'left')
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: None, reward: -5.51491095836
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': -5.51491095836379, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.51)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', 'right', 'left')
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: forward, reward: 1.95690978572
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.9569097857184983, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent drove forward instead of right. (rewarded 1.96)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: right, reward: 1.06518655976
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.0651865597556456, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.07)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: right, reward: 1.84305734274
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.8430573427367647, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.84)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: None, reward: 2.76431025117
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.764310251174595, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.76)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: right, reward: 0.220905009754
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.2209050097543277, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.22)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
2.21985244203
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: right, reward: 1.6724421285
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.6724421285027369, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.67)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 0.820737102644
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.8207371026436133, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.82)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: forward, reward: -9.75416978648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': -9.754169786483939, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.75)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'forward', 'right')
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: forward, reward: -10.1690287546
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -10.169028754603419, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -10.17)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: left, reward: -10.1729529951
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -10.17295299513539, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.17)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 1.95123474672
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.951234746716454, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.95)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', 'left')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: forward, reward: 0.246129745116
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.2461297451164638, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded 0.25)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: forward, reward: -9.29738773624
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -9.297387736239015, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.30)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 2.30484665589
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 2.304846655892506, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.30)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 1.65860374903
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.658603749030531, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.66)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
1.1562473629
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 0.651586531116
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 0.651586531116251, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.65)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: -0.513557377016
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -0.5135573770164747, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded -0.51)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', 'forward')
0.0
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: 1.03201300795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.032013007948232, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.03)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 47
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (6, 5), deadline = 25
0.836440408654
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
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epsilon = 0.8364; alpha = 0.5000
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epsilon = 0.8364; alpha = 0.5000
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Simulating trial. . .
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Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8364; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: 0.343212116236
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 0.3432121162356456, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.34)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.52335370317
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.5233537031686415, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.52)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'left')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: -9.45283834198
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -9.452838341975795, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.45)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: -9.07537692299
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': -9.07537692299234, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.08)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: -10.060203666
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': -10.060203666012354, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.06)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: -5.92375422934
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': -5.923754229338341, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.92)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: -4.24470107292
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 19, 't': 6, 'action': None, 'reward': -4.244701072924097, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.24)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'right', None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: -5.23438277325
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 18, 't': 7, 'action': None, 'reward': -5.234382773251793, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.23)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: left, reward: 2.61230804691
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 2.6123080469137623, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.61)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 0.224089566844
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.2240895668436922, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.22)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 0.0115727047338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 0.011572704733829986, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.01)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: None, reward: 2.1722164719
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.172216471904124, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.17)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: left, reward: -9.56187315149
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -9.561873151487289, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.56)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 0.145908424814
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 0.14590842481433763, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.15)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'right', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 1.57140477122
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.5714047712211432, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.57)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -10.8614348919
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -10.86143489192998, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.86)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 0.334437911081
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 0.33443791108067844, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.33)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'left')
1.12981995349
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 2.07368697949
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 8, 't': 17, 'action': None, 'reward': 2.0736869794853994, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.07)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'right', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: right, reward: -0.0264797587594
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': -0.026479758759412553, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded -0.03)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 0.821697663089
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 0.8216976630889202, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent drove right instead of forward. (rewarded 0.82)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, 'left')
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 1.08028028071
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.080280280706419, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded 1.08)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: -9.97195650091
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': -9.971956500914247, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.97)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: -9.94023911041
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': -9.940239110412813, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -9.94)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -39.3621950348
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -39.36219503479141, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.36)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: -10.1380269505
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': -10.138026950450342, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.14)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 48
\-------------------------
Environment.reset(): Trial set up with start = (4, 4), destination = (6, 7), deadline = 25
0.833267966559
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8333; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, None)
1.93083318451
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: 2.2651330507
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 2.265133050699813, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.27)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: left, reward: -40.4098295285
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -40.409829528458225, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.41)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 2.56549684981
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.5654968498078485, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.57)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
0.903916947006
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: forward, reward: 1.11858223539
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 1.118582235388932, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.12)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: forward, reward: -9.93056453761
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': -9.930564537611819, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.93)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: right, reward: 0.363966881196
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.36396688119640863, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.36)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: -4.46449550688
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 6, 'action': None, 'reward': -4.464495506875301, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.46)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: left, reward: -10.7555331822
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -10.75553318224625, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.76)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: right, reward: 1.65255877364
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.6525587736425438, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.65)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
2.17191889745
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: 2.79794377756
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 2.7979437775584124, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.80)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'left', None)
1.44989241761
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: 1.74908667484
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 1.749086674841123, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.75)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 0.386037408705
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 0.38603740870498815, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 0.39)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: -10.232114758
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': -10.232114757992225, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.23)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', 'left')
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: right, reward: 0.795256621108
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 0.7952566211082703, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 0.80)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, 'left')
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: left, reward: -10.79284086
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -10.792840860047985, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.79)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: -9.46012319028
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': -9.46012319027774, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -9.46)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', 'left')
1.50955275044
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: None, reward: 2.61618930311
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 9, 't': 16, 'action': None, 'reward': 2.6161893031088166, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.62)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', None)
2.17152909102
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: right, reward: 2.47538168847
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 2.475381688465399, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.48)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 49
\-------------------------
Environment.reset(): Trial set up with start = (2, 6), destination = (8, 3), deadline = 25
0.830107556867
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8301; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: forward, reward: -40.681448195
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': -40.68144819499588, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.68)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: right, reward: -20.5517489979
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': -20.55174899789149, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.55)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: right, reward: -19.8771204721
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': -19.877120472117106, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.88)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: forward, reward: 1.84973903161
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 1.8497390316096893, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 1.85)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: forward, reward: -39.6958602714
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': -39.69586027143509, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.70)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: left, reward: -9.44027935944
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -9.440279359440526, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -9.44)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 2.32817578069
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 2.3281757806941226, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.33)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: left, reward: 1.08873034999
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 1.0887303499905638, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.09)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: 0.666746340988
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 0.6667463409881397, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.67)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: 0.178491060332
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 0.17849106033237927, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 0.18)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 1.28623412946
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.2862341294619033, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.29)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
1.94614728526
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: 1.70008270975
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.7000827097477742, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.70)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: left, reward: -20.4662304272
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -20.466230427238404, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.47)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: -40.9389395284
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': -40.93893952840878, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.94)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: -39.7691343396
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': -39.76913433964967, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.77)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: right, reward: 0.917105510994
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.9171055109939457, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 0.92)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', None)
1.79592410519
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 2.33442760738
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 2.3344276073795722, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.33)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 1.68017633216
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.6801763321592476, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.68)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: 0.292046813158
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 7, 't': 18, 'action': 'left', 'reward': 0.29204681315838177, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 0.29)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: -39.7396077647
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 6, 't': 19, 'action': 'left', 'reward': -39.7396077647378, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.74)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: None, reward: 1.07855634498
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.0785563449793376, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.08)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 1.23875854786
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 1.2387585478607188, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.24)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: -10.5677140206
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': -10.567714020600862, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.57)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: right, reward: 1.18692390862
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.186923908620171, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.19)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: None, reward: 0.949477915512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 1, 't': 24, 'action': None, 'reward': 0.9494779155124484, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.95)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 50
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (2, 7), deadline = 25
0.826959133943
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8270; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
1.82311499751
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: right, reward: 2.62585232602
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 2.625852326015786, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.63)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: 0.146045564123
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 0.146045564122959, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.15)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'forward')
1.54425698016
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 0.992505795681
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.9925057956812624, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 0.99)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: -40.2132850326
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 22, 't': 3, 'action': 'left', 'reward': -40.21328503258774, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.21)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: -9.55749639871
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -9.557496398713212, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.56)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
1.4766936597
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.76334765957
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.7633476595701951, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.76)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: -19.8567301848
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -19.85673018482011, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.86)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: -4.70675647294
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': -4.706756472938785, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.71)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: -5.60987597575
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': -5.609875975747334, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.61)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'forward')
2.20212564161
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: left, reward: 0.99672835059
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 0.9967283505901277, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.00)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: -4.40652950786
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': -4.406529507856948, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.41)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: -4.63649826263
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': -4.6364982626322755, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.64)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: forward, reward: 2.67539800194
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 2.675398001938289, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.68)
48% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 51
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (1, 7), deadline = 20
0.823822652324
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8238; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: left, reward: -19.0353682903
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -19.035368290250606, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.04)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'forward')
1.5994269961
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: left, reward: 2.91022875995
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 2.9102287599506536, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.91)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: 1.40801866394
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.4080186639392236, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.41)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: -10.8567697568
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.85676975682469, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.86)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: 0.971991116502
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 0.9719911165015291, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.97)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 0.067941087799
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.06794108779902586, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.07)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: forward, reward: 1.13904537193
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.1390453719265632, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.14)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'right')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: 0.424624425239
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 0.42462442523944977, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove forward instead of left. (rewarded 0.42)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: -5.32038316891
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': -5.3203831689117305, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.32)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, 'left')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: -10.9659184986
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -10.965918498611963, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.97)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: right, reward: 1.32220851165
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.3222085116519113, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.32)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', 'left')
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 2.65470477441
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.654704774414455, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.65)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: -5.37034670271
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': -5.370346702711975, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.37)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
1.74131130554
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 1.84582396928
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.8458239692841094, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.85)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 0.692249022141
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.6922490221411977, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.69)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: -9.26397843429
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -9.263978434292586, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.26)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'forward')
1.26838138792
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 1.24218904291
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.242189042911388, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.24)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 0.857168554456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.8571685544560951, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.86)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'right', 'forward')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 0.349099616795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.34909961679495227, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 0.35)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'right', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: right, reward: 0.580398500284
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 0.5803985002844224, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.58)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 52
\-------------------------
Environment.reset(): Trial set up with start = (6, 2), destination = (8, 5), deadline = 25
0.820698066717
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8207; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: right, reward: -20.3517859667
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': -20.351785966710494, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.35)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: None, reward: 2.3606194128
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.3606194127955407, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.36)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: right, reward: 1.91709884702
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.9170988470241483, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.92)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: None, reward: 2.55849818506
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.5584981850649267, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.56)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: 2.87073108944
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 2.870731089444218, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.87)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 2.15532763827
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.1553276382702053, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.16)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
1.77277436342
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 1.959497076
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.9594970759967276, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.96)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 1.54940144764
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.5494014476445608, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.55)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 1.03871698762
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.038716987617304, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.04)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 1.00641661067
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.006416610672316, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.01)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'left', 'left')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 0.679237160108
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 0.6792371601081407, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent drove forward instead of right. (rewarded 0.68)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
2.22448366176
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 1.60725005061
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.6072500506078824, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.61)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
1.44541959653
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 2.77431935755
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 2.7743193575528386, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.77)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 1.34298335443
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.342983354427055, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.34)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 0.949239727485
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 0.9492397274847167, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.95)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 1.22809886184
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.2280988618374757, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.23)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'right', None)
1.63478865165
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 1.7902406515
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.7902406514955, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.79)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: -0.257745338364
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': -0.25774533836437596, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded -0.26)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: -10.0472583524
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -10.047258352361865, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.05)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
2.48493133751
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: left, reward: 2.12129411577
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': 2.1212941157747602, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.12)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, 'forward')
2.25482787802
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: left, reward: 1.21442649803
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 5, 't': 20, 'action': 'left', 'reward': 1.2144264980256252, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.21)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: left, reward: 0.558229900936
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 0.558229900936306, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.56)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: right, reward: 1.55583834805
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 1.5558383480459186, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.56)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', None, 'forward')
1.25528521542
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: right, reward: 1.27112651353
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.2711265135301366, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.27)
4% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 53
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (4, 7), deadline = 20
0.817585332006
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8176; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'right')
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -10.3204705624
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -10.320470562413798, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -10.32)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -9.36725429888
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -9.36725429888327, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.37)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -10.0763798631
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.076379863092534, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.08)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 2.65730327922
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.6573032792203755, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.66)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'left')
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: forward, reward: -10.7589288648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.758928864804838, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.76)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 2.8263920958
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.8263920957975763, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.83)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: -5.70070561812
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': -5.700705618116304, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.70)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'right')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: left, reward: 1.90255621704
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.9025562170390855, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.90)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: None, reward: -4.75379292828
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 12, 't': 8, 'action': None, 'reward': -4.753792928282931, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.75)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: 1.17752451317
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.1775245131670282, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.18)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: left, reward: -9.13332390634
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -9.133323906344582, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.13)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'left', None)
2.25440922918
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 1.4032202557
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.403220255702833, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.40)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: left, reward: -9.86884419253
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -9.86884419253239, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.87)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: left, reward: 1.40013155754
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.4001315575384812, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.40)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: left, reward: 0.631376655122
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 0.6313766551221177, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.63)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: left, reward: -9.249764234
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -9.24976423399513, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.25)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 1.07521340355
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 1.0752134035488587, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.08)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'right', 'right')
New state created!
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.21354844473
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'right'), 'deadline': 3, 't': 17, 'action': None, 'reward': 2.2135484447302387, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 2.21)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
1.83285391254
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.99959060731
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.9995906073061207, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.00)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: -9.25921150189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -9.259211501888265, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.26)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 54
\-------------------------
Environment.reset(): Trial set up with start = (1, 6), destination = (6, 2), deadline = 25
0.81448440324
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8145; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: 1.59007814483
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.5900781448332106, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.59)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: -5.66897388317
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': -5.668973883169116, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.67)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
1.91586685618
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 1.7126012047
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.7126012046972052, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.71)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
0.913226538265
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.21408415474
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.214084154735406, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 1.21)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 0.567521322967
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.5675213229670332, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.57)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 1.29931984634
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.299319846340722, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.30)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
1.91622225993
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: 1.04396349299
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.0439634929851471, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.04)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
1.81423403044
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 0.994884213244
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 0.9948842132441458, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.99)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
1.1699547656
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: -0.113537473293
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 17, 't': 8, 'action': 'right', 'reward': -0.11353747329347008, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded -0.11)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 0.58570099255
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.5857009925495877, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 0.59)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: left, reward: -10.0828317822
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -10.082831782174022, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.08)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 0.612386658009
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 14, 't': 11, 'action': None, 'reward': 0.6123866580085732, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded 0.61)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 1.70754677485
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.707546774847814, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded 1.71)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'right', 'forward')
New state created!
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: left, reward: -39.7145291782
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': -39.71452917824168, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.71)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: -9.28820856841
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': -9.288208568414401, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.29)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'left', 'forward')
1.00687005193
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 0.720652860584
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 10, 't': 15, 'action': None, 'reward': 0.7206528605835385, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded 0.72)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: -4.00706971178
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 9, 't': 16, 'action': None, 'reward': -4.00706971178168, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.01)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: -20.2496743738
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': -20.249674373781975, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.25)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: 0.859427109355
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 0.8594271093552048, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 0.86)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: left, reward: -0.449863026013
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 6, 't': 19, 'action': 'left', 'reward': -0.4498630260132913, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded -0.45)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 1.36560992473
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.3656099247286415, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.37)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, 'forward')
1.73462718803
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: left, reward: 0.913464346297
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 0.9134643462968197, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 0.91)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: 1.04180557343
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 1.04180557343451, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.04)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: left, reward: -19.1572034744
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -19.157203474384254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.16)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: forward, reward: 1.97349035823
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': 1.973490358226274, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.97)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 55
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (2, 2), deadline = 20
0.811395235643
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8114; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: -9.23859422424
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -9.238594224235428, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.24)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: -9.8753820642
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -9.875382064200958, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.88)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: right, reward: 1.65558853328
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.6555885332820446, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.66)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: forward, reward: -9.33141085532
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -9.331410855322606, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.33)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'forward')
2.37011907666
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.31046364455
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.3104636445525086, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.31)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 0.601066800394
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.6010668003935435, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.60)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -9.03264650097
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -9.032646500969197, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.03)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -9.64054092728
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -9.640540927282599, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.64)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
2.46286499398
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: None, reward: 1.43428115312
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.4342811531202997, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.43)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -10.9636736985
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -10.963673698517061, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.96)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: -10.6246295496
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -10.624629549623732, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.62)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 1.53057071069
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.5305707106888347, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.53)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: left, reward: -10.5257132542
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -10.525713254224511, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.53)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: 0.100746703285
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.10074670328489932, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 0.10)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: forward, reward: 0.323304925071
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.32330492507100106, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 0.32)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', None)
1.21134433014
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 2.10751902219
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 2.107519022187756, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.11)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: left, reward: -39.9904746497
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -39.990474649685176, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.99)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: right, reward: 0.208358860733
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.20835886073258936, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.21)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 0.292522431866
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.2925224318657009, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.29)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: -5.32874521527
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': -5.328745215271108, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.33)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 56
\-------------------------
Environment.reset(): Trial set up with start = (4, 5), destination = (6, 3), deadline = 20
0.808317784608
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8083; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'right', 'forward')
New state created!
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: right, reward: -19.7603319602
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': -19.760331960214387, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.76)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: forward, reward: -10.54652267
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -10.54652267000095, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -10.55)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: right, reward: 0.476214350094
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 0.47621435009442614, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.48)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: None, reward: -4.36820706643
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': -4.368207066433277, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.37)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'forward', None)
0.587050073404
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: right, reward: 0.70589316198
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.7058931619798557, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.71)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
1.81762093769
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: 1.12074887468
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.1207488746766954, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.12)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: -20.4567529866
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': -20.456752986637596, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.46)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: -5.29579851861
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': -5.295798518610663, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.30)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
2.30311272664
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 1.80320668216
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 1.8032066821555393, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.80)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: -9.16102796186
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -9.161027961864923, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.16)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'right', 'forward')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: -39.8844509063
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -39.88445090629682, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.88)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: -0.0541628241626
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': -0.054162824162639955, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded -0.05)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: forward, reward: -9.30396823166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -9.303968231658397, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.30)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: -19.4300574653
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -19.43005746527187, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.43)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'right')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 0.167833517652
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.16783351765154142, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove right instead of left. (rewarded 0.17)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', 'right')
0.560809628664
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 0.386982206986
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.3869822069862685, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent drove right instead of left. (rewarded 0.39)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 2.06622660495
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 16, 'action': None, 'reward': 2.066226604949717, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: -10.2707186303
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -10.270718630290773, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.27)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: -40.9542969941
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': -40.95429699407159, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.95)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 1.79679726003
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 1.7967972600328699, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.80)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 57
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (3, 4), deadline = 20
0.805252005696
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8053; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'left')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: left, reward: -10.5742416275
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -10.574241627460012, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -10.57)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
1.65943167617
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: right, reward: 2.21359693113
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.213596931125413, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.21)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: -10.9305247043
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.930524704307993, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.93)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: right, reward: 0.565468606913
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.565468606913445, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.57)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
1.94857307355
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: None, reward: 2.48259663268
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.4825966326773377, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.48)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: -9.48324521786
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -9.48324521785534, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.48)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: -10.1662340103
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -10.166234010341691, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.17)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 0.596660565593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 0.5966605655934698, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.60)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'right', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: -9.37469529975
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.374695299747081, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.37)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: -4.88817946584
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': None, 'reward': -4.888179465839997, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.89)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 0.589373257058
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 0.5893732570581044, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.59)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: -0.0933686011551
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -0.09336860115513024, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded -0.09)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: left, reward: 2.08347700701
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 2.0834770070109947, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.08)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
1.0112495912
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 1.02518207115
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.0251820711549604, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.03)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 0.960984883272
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.9609848832720572, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.96)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: -5.7743735654
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': -5.774373565397702, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.77)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: -0.289420153466
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': -0.28942015346645467, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded -0.29)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: -5.21625150668
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': -5.216251506680243, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.22)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, 'right')
1.71904466877
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: 1.87370542695
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': 1.8737054269477116, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.87)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: right, reward: -0.086869442725
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': -0.08686944272501707, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.09)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 58
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (6, 3), deadline = 20
0.802197854636
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
Simulating trial. . .
epsilon = 0.8022; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 2.11120800089
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.1112080008870064, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.11)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'forward', 'right')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 2.50374204612
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'right'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.503742046119224, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'right')
Agent followed the waypoint right. (rewarded 2.50)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.1245346782
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.1245346782026608, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.12)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -10.481915697
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.481915697026428, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.48)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -10.0973220862
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.097322086204272, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -10.10)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -10.5773649002
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -10.577364900245463, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.58)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 0.946241136981
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 0.9462411369805352, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.95)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: left, reward: -10.6314277255
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -10.631427725512038, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.63)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: -10.3197857375
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -10.319785737458883, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.32)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 0.30017672116
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.3001767211599975, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove right instead of left. (rewarded 0.30)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: forward, reward: -10.968089876
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -10.968089875990582, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.97)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: left, reward: 0.0674504642987
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 0.0674504642987126, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.07)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
2.21558485311
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 0.675667024565
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.6756670245646568, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.68)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: -4.65600422728
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': -4.656004227283696, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.66)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: left, reward: 2.21208173385
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 2.2120817338473198, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.21)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: -4.76564565512
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': -4.765645655120077, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.77)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'right', None)
0.924332984613
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: right, reward: 0.999768351729
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 0.9997683517286153, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.00)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: left, reward: 0.717904601688
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 0.7179046016876365, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.72)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', 'right', 'left')
0.318948332061
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: -0.102510852903
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': -0.10251085290275563, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove right instead of left. (rewarded -0.10)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'left', None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 0.223006440943
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.22300644094318922, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.22)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 59
\-------------------------
Environment.reset(): Trial set up with start = (6, 3), destination = (1, 2), deadline = 20
0.799155287328
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7992; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: right, reward: 0.221873577147
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 0.22187357714685696, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 0.22)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: forward, reward: 0.355537071783
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 0.355537071782711, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 0.36)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: left, reward: -10.3853627531
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.385362753102402, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.39)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.49533519896
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: 2.31608720293
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.316087202930368, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.32)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: forward, reward: -10.0151943924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.01519439238802, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.02)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: -4.97862775502
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': -4.978627755020069, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.98)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: -4.37421372509
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': -4.374213725090476, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.37)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: left, reward: 0.640145190284
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 0.6401451902838071, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.64)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'right')
1.50832565858
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 2.6241537337
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 2.6241537337041354, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 2.62)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: -10.2296535412
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': -10.229653541179072, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.23)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 2.68328454038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.6832845403835073, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.68)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'right', 'forward')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 1.01436330135
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.0143633013546223, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.01)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: -10.3293032632
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -10.329303263225407, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.33)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.97076818097
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: 1.64002256884
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.640022568843975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.64)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'forward')
1.09033265751
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 0.981940290334
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.9819402903335233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.98)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: 1.75466089612
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.754660896119072, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.75)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: 1.40029457348
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 1.400294573484475, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.40)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: -39.8862775794
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -39.8862775793837, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.89)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 1.02363611183
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.0236361118312598, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.02)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: -10.9552310114
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': -10.955231011429586, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.96)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 60
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (6, 4), deadline = 20
0.796124259835
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7961; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: -20.6101812412
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': -20.610181241245364, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.61)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: None, reward: 2.43030776166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.4303077616551705, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.43)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: -39.9694119484
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -39.969411948398694, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.97)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: -10.6178522073
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.617852207312557, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.62)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 2.73617669905
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.736176699048064, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.74)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: -5.41896394163
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': -5.418963941627479, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.42)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 0.153457667656
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.15345766765613988, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 0.15)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: -5.09092718973
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 13, 't': 7, 'action': None, 'reward': -5.090927189733835, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.09)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: right, reward: 0.381405011377
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 0.38140501137702276, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded 0.38)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'left')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: 2.38962154437
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 2.3896215443656676, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.39)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'left')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: -10.5567735977
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -10.55677359770117, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.56)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: 2.43296409372
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.432964093716441, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.43)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 1.4308032932
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.4308032931976729, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.43)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: -40.7241078082
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -40.72410780819001, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.72)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: -40.4404279637
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -40.440427963657264, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.44)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 0.542605135808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.5426051358082602, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.54)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: -20.9387948716
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -20.938794871574018, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.94)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 0.739730920834
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.7397309208338542, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.74)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: right, reward: -0.240716400008
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': -0.24071640000781702, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded -0.24)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'right', None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 0.916153082671
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': 0.9161530826709287, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'right', None)
Agent drove forward instead of right. (rewarded 0.92)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 61
\-------------------------
Environment.reset(): Trial set up with start = (1, 6), destination = (5, 2), deadline = 30
0.793104728391
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7931; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
2.01824186277
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 1.24174727892
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.2417472789248754, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.24)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
1.84558759988
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 1.53365759347
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 1.5336575934704801, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.53)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 0.251271901938
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.2512719019378028, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent drove right instead of forward. (rewarded 0.25)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: 2.19548773813
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 27, 't': 3, 'action': 'left', 'reward': 2.195487738133423, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.20)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 0.580556665971
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 26, 't': 4, 'action': 'left', 'reward': 0.5805566659707627, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.58)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: 1.03664070331
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'left', 'reward': 1.0366407033107339, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded 1.04)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'left', 'forward')
0.706533893727
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 2.82215830967
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.8221583096653733, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.82)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 1.14440346645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 1.144403466449175, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.14)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
2.09894299469
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 2.05893595297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.058935952966128, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.06)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: right, reward: 2.02920202015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 2.0292020201508163, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.03)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.13824961903
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.138249619034859, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.14)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 1.59975311054
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 1.59975311053774, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.60)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'left')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: 1.97697793451
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 18, 't': 12, 'action': 'left', 'reward': 1.9769779345054315, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.98)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: -39.1491558519
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': -39.14915585191434, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.15)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'right', 'left')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: -0.20760737071
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 16, 't': 14, 'action': 'right', 'reward': -0.20760737071002788, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent drove right instead of forward. (rewarded -0.21)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 1.80343315006
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.8034331500635545, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.80)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: left, reward: 2.01876184377
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 2.018761843768803, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.02)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: right, reward: -0.0363143473599
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 17, 'action': 'right', 'reward': -0.03631434735993211, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded -0.04)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: None, reward: 2.19951184616
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 18, 'action': None, 'reward': 2.1995118461634284, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.20)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: left, reward: -20.6678428324
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 11, 't': 19, 'action': 'left', 'reward': -20.667842832369136, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.67)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', 'forward', 'forward')
1.4485351185
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: forward, reward: 1.99672339258
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 10, 't': 20, 'action': 'forward', 'reward': 1.9967233925848409, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 2.00)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 2.04930697038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 21, 'action': None, 'reward': 2.0493069703815503, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.05)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: right, reward: 1.04198416408
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 1.041984164084721, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.04)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', None, 'right')
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: right, reward: 0.715874434915
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 7, 't': 23, 'action': 'right', 'reward': 0.7158744349153486, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove right instead of left. (rewarded 0.72)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: forward, reward: -39.0034265856
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 6, 't': 24, 'action': 'forward', 'reward': -39.00342658555345, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.00)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: left, reward: 0.0384549998837
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 5, 't': 25, 'action': 'left', 'reward': 0.038454999883731356, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 0.04)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 0.423442097416
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 26, 'action': 'forward', 'reward': 0.42344209741646166, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.42)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: right, reward: 1.21380646395
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 3, 't': 27, 'action': 'right', 'reward': 1.2138064639483748, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.21)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: -9.43198711141
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 2, 't': 28, 'action': 'forward', 'reward': -9.43198711140769, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -9.43)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: -10.5846765527
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 1, 't': 29, 'action': 'forward', 'reward': -10.584676552656633, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.58)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 62
\-------------------------
Environment.reset(): Trial set up with start = (2, 6), destination = (6, 3), deadline = 35
0.790096649393
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7901; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'right', None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: -4.84647634297
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 35, 't': 0, 'action': None, 'reward': -4.84647634297175, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.85)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: 1.95874221971
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 34, 't': 1, 'action': 'right', 'reward': 1.958742219705008, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.96)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: forward, reward: -10.4345253294
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 33, 't': 2, 'action': 'forward', 'reward': -10.434525329411514, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.43)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
2.12862488294
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 1.12182911444
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 32, 't': 3, 'action': None, 'reward': 1.121829114438244, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.12)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.62522699869
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 2.81393676527
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 31, 't': 4, 'action': None, 'reward': 2.813936765273678, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.81)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: -10.8937148773
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 30, 't': 5, 'action': 'left', 'reward': -10.89371487730369, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.89)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 1.64592700988
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 29, 't': 6, 'action': 'forward', 'reward': 1.6459270098785006, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.65)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: forward, reward: 1.26673801945
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 28, 't': 7, 'action': 'forward', 'reward': 1.2667380194499085, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.27)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'forward')
1.55015467625
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 2.26906349907
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 27, 't': 8, 'action': None, 'reward': 2.26906349907199, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.27)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: right, reward: 1.90821024215
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 9, 'action': 'right', 'reward': 1.9082102421460334, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.91)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'right', 'right')
New state created!
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: None, reward: -4.25280442607
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', 'right'), 'deadline': 25, 't': 10, 'action': None, 'reward': -4.252804426067314, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.25)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: forward, reward: 0.959966527694
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 11, 'action': 'forward', 'reward': 0.9599665276940764, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.96)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: forward, reward: 0.482749925018
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 23, 't': 12, 'action': 'forward', 'reward': 0.48274992501827974, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.48)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: forward, reward: -39.8993418213
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 22, 't': 13, 'action': 'forward', 'reward': -39.89934182134147, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.90)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
1.44562593884
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 2.74314615123
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 14, 'action': None, 'reward': 2.743146151225601, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.74)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: forward, reward: -40.1271034705
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 20, 't': 15, 'action': 'forward', 'reward': -40.12710347050398, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.13)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
1.81227003068
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: left, reward: 2.26022196768
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 16, 'action': 'left', 'reward': 2.2602219676804145, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.26)
51% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 63
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (6, 6), deadline = 25
0.787099979405
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7871; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: 0.491859429903
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 0.4918594299026504, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove forward instead of left. (rewarded 0.49)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: 1.40626584191
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 1.4062658419055418, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.41)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: forward, reward: -40.6666705976
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -40.666670597624524, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.67)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.81612394625
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 2.37524885026
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.3752488502559466, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.38)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: -40.670689136
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -40.6706891359742, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.67)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 0.491680513415
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.4916805134145962, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.49)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', 'right')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: -4.6131770867
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', 'right'), 'deadline': 19, 't': 6, 'action': None, 'reward': -4.613177086704336, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.61)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: -10.7440218168
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': -10.744021816770259, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.74)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', None)
0.957382573528
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 2.5276071417
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.527607141698191, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.53)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: -10.4147176919
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': -10.414717691859915, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -10.41)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: -5.4618043437
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 15, 't': 10, 'action': None, 'reward': -5.4618043437012584, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.46)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'left')
1.0636553465
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 1.48102243898
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.4810224389799331, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 1.48)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
2.09568639825
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 2.09546571722
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.0954657172239344, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.10)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 1.54108416141
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 1.5410841614149555, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.54)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.52275386685
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.5227538668500102, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.52)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 0.799590682014
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.7995906820135308, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.80)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
0.982228484078
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 0.658214155202
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 0.6582141552023733, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.66)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: -4.63955018695
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 8, 't': 17, 'action': None, 'reward': -4.639550186945174, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.64)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: -19.8753128729
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 7, 't': 18, 'action': 'left', 'reward': -19.875312872866765, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.88)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 0.830483961657
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 6, 't': 19, 'action': None, 'reward': 0.8304839616567441, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.83)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 2.24952577773
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 5, 't': 20, 'action': None, 'reward': 2.2495257777312965, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.25)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: 0.676079029435
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 0.6760790294345759, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.68)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 0.80735440727
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 0.8073544072696988, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.81)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: left, reward: -9.51699510537
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -9.516995105366254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -9.52)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: right, reward: 0.704226300662
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.7042263006615341, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.70)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 64
\-------------------------
Environment.reset(): Trial set up with start = (1, 4), destination = (7, 2), deadline = 20
0.784114675153
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7841; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: -4.34504845748
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 20, 't': 0, 'action': None, 'reward': -4.345048457477316, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.35)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: -4.04724282354
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 19, 't': 1, 'action': None, 'reward': -4.047242823538201, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.05)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'right')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: 1.73720760326
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.7372076032560386, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove forward instead of left. (rewarded 1.74)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 0.113128296458
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.11312829645800304, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.11)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: -10.6892626857
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -10.689262685699948, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.69)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: -9.51842616865
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -9.518426168645833, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.52)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: -5.69456860892
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': None, 'reward': -5.694568608922303, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.69)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: -5.59903582726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': -5.599035827259512, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.60)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 1.52037971479
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.5203797147916247, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 1.52)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: 2.03406738125
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 2.0340673812540695, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.03)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 1.39221560368
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.3922156036774362, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.39)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: -9.31522532173
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -9.315225321731361, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.32)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: -10.8919655349
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -10.891965534915014, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.89)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: 0.373103372074
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.37310337207406163, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.37)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'right', 'right')
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: 0.996491469576
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.9964914695761129, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 1.00)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: left, reward: -9.25156513169
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -9.251565131689185, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.25)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: -4.79502126568
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': -4.795021265683933, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.80)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: -5.86899162977
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 3, 't': 17, 'action': None, 'reward': -5.868991629766538, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.87)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: left, reward: 0.34282951756
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': 0.3428295175598892, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.34)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 0.348642199045
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 0.3486421990448405, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.35)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 65
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (8, 2), deadline = 25
0.781140693531
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
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epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
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Simulating trial. . .
epsilon = 0.7811; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: left, reward: -39.1826231459
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -39.182623145906916, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.18)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: right, reward: 0.143931602558
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.14393160255752413, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.14)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: None, reward: 1.62326858862
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.6232685886221232, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.62)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: None, reward: -5.42885346842
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': -5.428853468422753, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.43)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'left')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: forward, reward: 0.310929568188
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 0.3109295681878478, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 0.31)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'right', None)
0.962050668171
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 0.610570308243
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.6105703082429242, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.61)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: -0.0752569562593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -0.07525695625934825, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded -0.08)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: right, reward: 2.42242253412
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 2.4224225341243235, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.42)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: forward, reward: -9.99255850832
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -9.99255850831759, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -9.99)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 2.52070375596
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.520703755956105, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.52)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.53606669718
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: 2.48286474017
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.482864740170995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.48)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 0.554305912001
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 0.5543059120013679, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.55)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
1.72125592054
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: 2.42904433128
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 2.429044331279858, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.43)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: right, reward: 1.24936481546
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 1.2493648154572097, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent drove right instead of forward. (rewarded 1.25)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: 1.5465843529
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': 1.5465843529021421, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.55)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: -9.52517915318
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -9.525179153177389, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.53)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: -20.55116713
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -20.551167129963538, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.55)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 0.0187092799545
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 0.01870927995445637, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.02)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: -10.0643975294
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 18, 'action': 'left', 'reward': -10.064397529419365, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.06)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: -19.2053248655
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': -19.20532486549711, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.21)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: -39.2266106517
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': -39.226610651699126, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.23)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: right, reward: 0.553149427979
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 0.5531494279788499, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove right instead of left. (rewarded 0.55)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: forward, reward: 0.213982886747
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.2139828867472935, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.21)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, 'forward')
0.0
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.43860388145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 2, 't': 23, 'action': None, 'reward': 1.4386038814508213, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.44)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, 'forward')
0.719301940725
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.85727404148
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 1, 't': 24, 'action': None, 'reward': 1.8572740414800393, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.86)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 66
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (7, 6), deadline = 25
0.778177991595
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7782; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
1.81086723941
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: 1.95300773214
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.953007732144255, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.95)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: 0.516318901426
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 0.5163189014260459, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.52)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
2.09557605774
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 1.08641162688
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.0864116268824873, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.09)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: 0.0487298745211
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 0.048729874521104444, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.05)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: left, reward: -39.4632172972
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -39.463217297180734, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.46)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', 'right')
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 0.118510736568
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.11851073656829902, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove right instead of left. (rewarded 0.12)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: 2.10897587077
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 2.1089758707675132, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.11)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'right', 'forward')
New state created!
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: right, reward: 0.995704278813
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 0.9957042788131187, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent followed the waypoint right. (rewarded 1.00)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
2.13628352988
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: 2.14130067921
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.141300679206693, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.14)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: right, reward: 0.668760270435
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.6687602704345039, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.67)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'right', None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: 2.01622316563
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.016223165625581, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.02)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: left, reward: -9.10350313609
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -9.10350313609144, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.10)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 1.5314802026
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.5314802025959275, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.53)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: -19.7062514684
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': -19.70625146836303, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.71)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
1.93893530948
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: 0.998081430429
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.9980814304292032, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.00)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: left, reward: 0.776386933875
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 0.7763869338750053, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 0.78)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
2.37014281897
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 0.868261854273
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': 0.8682618542727407, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.87)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: 1.0321074636
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 1.0321074635983947, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.03)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 0.926370253944
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 0.9263702539437171, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.93)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 0.783563539933
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 0.7835635399333181, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.78)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: forward, reward: -9.44159536514
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': -9.441595365137715, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.44)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.31298974226
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 1.3129897422592447, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 1.31)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: forward, reward: 0.769397731854
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.7693977318536704, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.77)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: right, reward: 0.924949198868
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 0.9249491988677718, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.92)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, 'forward')
1.89809140172
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: 0.644035390775
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.6440353907749821, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 0.64)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 67
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (3, 3), deadline = 20
0.775226526562
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7752; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: forward, reward: 1.12462582485
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.1246258248525967, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 1.12)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'right', None)
0.786310488207
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 0.0211823125804
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.02118231258037151, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.02)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: -10.8336756021
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.833675602070985, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.83)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: -9.18471301588
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -9.184713015881115, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.18)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: -9.79160643968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -9.791606439679125, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.79)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 0.823814050115
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.8238140501149995, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.82)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'forward')
1.2882879911
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 2.78651642577
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.786516425772448, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.79)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'right', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: -0.0761373781789
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': -0.07613737817889432, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent drove forward instead of right. (rewarded -0.08)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: -4.66787923564
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': -4.66787923564314, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.67)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'right')
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: forward, reward: 1.61854423408
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.6185442340842933, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent drove forward instead of right. (rewarded 1.62)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
1.2781824124
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 1.53888636846
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.538886368463204, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.54)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'left')
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 2.1948948001
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.1948948000961175, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.19)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'right', None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: left, reward: -40.5027696862
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -40.502769686218464, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.50)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: left, reward: -19.2743971584
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -19.27439715841528, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.27)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'right', 'right')
New state created!
0.0
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: forward, reward: 1.0862419132
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'right'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 1.086241913202017, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'right')
Agent drove forward instead of right. (rewarded 1.09)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'left')
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 1.13755084333
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.1375508433336747, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.14)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: 0.893067875906
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 0.8930678759061115, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.89)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'right', 'forward')
0.0
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 1.64153133096
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.6415313309641792, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.64)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'right')
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: -10.1282480815
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -10.128248081484248, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -10.13)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: -9.08400880456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -9.084008804564094, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.08)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 68
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (2, 6), deadline = 20
0.772286255813
Simulating trial. . .
epsilon = 0.7723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7723; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: -39.5324590674
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -39.53245906735024, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.53)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: forward, reward: -9.24236209922
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -9.242362099216054, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.24)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: -10.5692140843
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.569214084308673, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.57)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: -4.07175900102
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': -4.071759001022887, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.07)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
0.796572369033
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 2.30094245727
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 2.3009424572655837, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.30)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: 0.91361981141
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 0.9136198114100422, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded 0.91)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'forward')
1.3410317419
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 0.92392753026
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 0.9239275302600742, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.92)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: -4.46749773993
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': -4.467497739932971, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.47)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: 2.05438391253
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 2.0543839125298846, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.05)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 2.78349516297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.783495162973607, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.78)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 0.267107844009
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.26710784400855625, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.27)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: 1.68559204011
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 1.6855920401050315, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.69)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: left, reward: -0.219478526696
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -0.21947852669590273, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded -0.22)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 1.82278834178
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.82278834177894, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.82)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 0.558963682644
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.5589636826439364, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 0.56)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 69
\-------------------------
Environment.reset(): Trial set up with start = (2, 6), destination = (6, 5), deadline = 25
0.769357136892
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7694; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: 2.07178497515
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.071784975154819, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: 2.54743869335
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 2.547438693352955, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.55)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: right, reward: 1.65986582828
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.6598658282802907, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.66)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'left')
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: forward, reward: 0.734384113783
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 0.7343841137829448, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 0.73)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: right, reward: 0.0296734281844
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.02967342818440566, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.03)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 0.18496038705
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 0.18496038704976037, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.18)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: 1.92038974696
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.9203897469550064, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.92)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, 'right')
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: left, reward: -9.76605943867
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -9.766059438671384, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -9.77)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 1.51397544523
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.5139754452290857, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.51)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 2.11493020446
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 2.1149302044613334, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.11)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 1.02455197793
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.0245519779326946, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.02)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: 2.29600625101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.2960062510061356, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.30)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
2.07741678392
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: 2.15754166765
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.157541667654855, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.16)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: 2.6579173618
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 2.6579173618006227, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.66)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', None)
1.31693511159
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 0.759805643127
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 11, 't': 14, 'action': None, 'reward': 0.7598056431265052, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.76)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: -9.82651211273
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -9.826512112732297, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.83)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: -4.90567562102
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 9, 't': 16, 'action': None, 'reward': -4.905675621021096, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.91)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 1.43682253563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.436822535626631, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 1.44)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: -0.151610913771
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': -0.15161091377078595, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.15)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'right', None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: -0.36552644034
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': -0.36552644033953896, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove forward instead of left. (rewarded -0.37)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: left, reward: 1.47289605993
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 5, 't': 20, 'action': 'left', 'reward': 1.4728960599345866, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.47)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 1.08578462595
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 21, 'action': None, 'reward': 1.0857846259487614, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.09)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 1.11961774698
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 22, 'action': None, 'reward': 1.1196177469787993, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.12)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: -9.31024010611
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 2, 't': 23, 'action': 'forward', 'reward': -9.31024010610531, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.31)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 0.359721918482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 1, 't': 24, 'action': None, 'reward': 0.35972191848249513, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.36)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 70
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (8, 6), deadline = 30
0.766439127501
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
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epsilon = 0.7664; alpha = 0.5000
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epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
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epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7664; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: forward, reward: -10.6898936074
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': -10.689893607431006, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.69)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: left, reward: -10.2758561487
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 29, 't': 1, 'action': 'left', 'reward': -10.27585614872254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.28)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: right, reward: 0.26884190102
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.2688419010198503, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded 0.27)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 1.40219087235
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.4021908723527947, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.40)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'right', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 1.33729568645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 1.3372956864524803, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 1.34)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
0.870734590604
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 1.2045114047
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.2045114047045815, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.20)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', 'right')
New state created!
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: right, reward: 0.734991141939
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 0.7349911419385599, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent drove right instead of forward. (rewarded 0.73)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', None)
1.46918490618
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: 2.58684505532
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 2.5868450553211755, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.59)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: forward, reward: -10.0785206038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': -10.078520603835402, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -10.08)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: forward, reward: 2.47088927231
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 2.470889272312133, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.47)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
1.61920233662
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 2.13712053046
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.1371205304617495, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.14)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 1.92530776855
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.9253077685528004, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.93)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'left')
1.91827512528
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: 2.23209352563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 18, 't': 12, 'action': 'left', 'reward': 2.2320935256260626, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.23)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'right', None)
1.74249485761
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.2102269778
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 17, 't': 13, 'action': None, 'reward': 1.210226977802347, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.21)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: -10.8386465492
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': -10.83864654920256, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.84)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: -9.21224486261
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': -9.212244862605665, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.21)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'right')
0.892738126483
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: 0.36344610905
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 0.3634461090495644, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent drove left instead of forward. (rewarded 0.36)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.12068813069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 13, 't': 17, 'action': None, 'reward': 2.1206881306925505, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.12)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'left')
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 0.989540382334
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 12, 't': 18, 'action': None, 'reward': 0.9895403823338944, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.99)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.47100424079
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 19, 'action': None, 'reward': 1.4710042407941475, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.47)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 2.04361621367
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 2.043616213673952, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent followed the waypoint right. (rewarded 2.04)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: left, reward: -10.8169856295
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 21, 'action': 'left', 'reward': -10.81698562948656, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.82)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: None, reward: 1.0587113351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 8, 't': 22, 'action': None, 'reward': 1.0587113350989776, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.06)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'left', 'left')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: 0.724926350419
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 7, 't': 23, 'action': 'left', 'reward': 0.7249263504187199, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent drove left instead of right. (rewarded 0.72)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 2.25066086246
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 6, 't': 24, 'action': None, 'reward': 2.250660862463287, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.25)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 1.49073794559
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 5, 't': 25, 'action': 'right', 'reward': 1.490737945589244, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.49)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 0.57411505875
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 4, 't': 26, 'action': 'right', 'reward': 0.5741150587503832, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent followed the waypoint right. (rewarded 0.57)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'green', 'forward', 'right')
New state created!
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 0.78851058912
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 3, 't': 27, 'action': 'right', 'reward': 0.7885105891200979, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent drove right instead of forward. (rewarded 0.79)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 0.536525020097
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 2, 't': 28, 'action': 'right', 'reward': 0.5365250200972244, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.54)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -40.8105507282
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 1, 't': 29, 'action': 'left', 'reward': -40.81055072819468, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.81)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 71
\-------------------------
Environment.reset(): Trial set up with start = (6, 2), destination = (5, 5), deadline = 20
0.763532185504
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7635; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: forward, reward: 1.47645757718
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.4764575771797348, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 1.48)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
1.59099384231
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: None, reward: 1.79046449395
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.7904644939492287, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.79)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: left, reward: -9.69075472225
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -9.690754722252304, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.69)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: forward, reward: 0.207723862682
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 0.20772386268220833, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.21)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: left, reward: 1.8573168737
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 1.8573168737032864, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.86)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, 'forward')
2.07904516957
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 0.940384418132
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 0.9403844181316026, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.94)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
1.83673732415
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 1.15646464111
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.156464641111984, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.16)
65% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 72
\-------------------------
Environment.reset(): Trial set up with start = (4, 4), destination = (1, 7), deadline = 30
0.760636268926
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7606; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'left')
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: left, reward: 1.09146623018
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 1.091466230184403, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.09)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: left, reward: 1.09014296921
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 29, 't': 1, 'action': 'left', 'reward': 1.0901429692113147, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.09)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 2.39613163088
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.396131630879906, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.40)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 2.60984948287
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.6098494828692758, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.61)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: left, reward: 1.90984804488
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': 1.9098480448798516, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 1.91)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: None, reward: 2.38380070755
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 25, 't': 5, 'action': None, 'reward': 2.383800707553841, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.38)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'left')
1.85201198465
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: None, reward: 2.63697741248
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.636977412477447, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.64)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
1.49660098263
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: right, reward: 1.19520249426
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 1.1952024942558759, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.20)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: -4.32774145761
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': -4.327741457607246, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.33)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.87816143354
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 0.954196541874
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': 0.9541965418741092, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.95)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 2.69893329633
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.6989332963331565, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.70)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: -9.05100909413
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': -9.05100909413006, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.05)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: left, reward: -10.0748644728
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': -10.074864472823183, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.07)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: right, reward: 0.921249863991
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 0.9212498639913418, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 0.92)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: right, reward: 1.31288614113
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 1.312886141127869, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent drove right instead of left. (rewarded 1.31)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: 1.16450261576
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 15, 'action': 'right', 'reward': 1.1645026157569691, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.16)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
1.36098579888
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: None, reward: 1.55894843755
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.5589484375497369, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.56)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: forward, reward: 1.61356892202
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 1.6135689220163065, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.61)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'left', 'left')
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: left, reward: 0.547502502082
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 12, 't': 18, 'action': 'left', 'reward': 0.5475025020820548, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent drove left instead of right. (rewarded 0.55)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', None)
1.73499577931
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: None, reward: 2.5517980887
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.551798088703551, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.55)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: None, reward: 1.17704595605
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 10, 't': 20, 'action': None, 'reward': 1.1770459560545032, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.18)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: forward, reward: 0.486736428246
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 0.48673642824572017, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 0.49)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', 'left', None)
2.51924355759
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.08239433126
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 22, 'action': None, 'reward': 1.0823943312571436, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.08)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.39329036858
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 23, 'action': None, 'reward': 1.3932903685777172, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.39)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: -0.519390061059
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 6, 't': 24, 'action': 'right', 'reward': -0.5193900610593216, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent drove right instead of forward. (rewarded -0.52)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.18200003809
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 5, 't': 25, 'action': None, 'reward': 1.1820000380894782, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.18)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: 0.446384958768
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 26, 'action': 'left', 'reward': 0.4463849587675224, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.45)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 1.21330896988
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 3, 't': 27, 'action': 'forward', 'reward': 1.2133089698826303, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.21)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 0.691967122963
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 2, 't': 28, 'action': None, 'reward': 0.691967122962973, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.69)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: left, reward: -0.143753886309
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 29, 'action': 'left', 'reward': -0.14375388630947206, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded -0.14)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 73
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (5, 7), deadline = 25
0.757751335948
Simulating trial. . .
epsilon = 0.7578; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7578; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7578; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7578; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7578; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: right, reward: 0.824845610997
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.8248456109968656, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.82)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'forward')
1.11087808515
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 2.74499878496
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.744998784964011, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.74)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 2.29174175442
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.2917417544156518, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.29)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.99123546127
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 1.8113878167
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.8113878167032007, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.81)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 2.4894130179
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.489413017904667, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.49)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', 'right')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 1.44018981151
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.440189811512255, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove right instead of left. (rewarded 1.44)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 1.33188068297
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.331880682969532, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.33)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: left, reward: -20.8331868338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -20.833186833848483, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.83)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: -4.8098287758
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': -4.8098287757963885, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.81)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: -4.30173747508
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': None, 'reward': -4.301737475080155, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.30)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 2.7122469086
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.7122469086026406, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 2.71)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: -4.72516879827
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': -4.725168798270341, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.73)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 0.00551768314905
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 0.005517683149053987, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.01)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: -4.45851608731
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': -4.458516087307481, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.46)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: 0.684729719303
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 0.684729719302721, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.68)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', None)
1.62002065964
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 1.09094881624
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.0909488162402816, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.09)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: 1.03508692582
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 1.0350869258221804, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.04)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'left')
1.60175346649
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: None, reward: 0.892888531353
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 8, 't': 17, 'action': None, 'reward': 0.8928885313529793, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.89)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: None, reward: 2.35096261564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 7, 't': 18, 'action': None, 'reward': 2.350962615638167, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.35)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: None, reward: 0.714126771927
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 0.7141267719274456, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.71)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: left, reward: -10.7246816648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 20, 'action': 'left', 'reward': -10.724681664820348, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.72)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: forward, reward: 0.471073670184
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.47107367018418633, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.47)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, 'right')
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: -5.68362978403
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 3, 't': 22, 'action': None, 'reward': -5.683629784025339, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.68)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', None, 'left')
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: left, reward: 1.75026873378
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': 1.750268733779466, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.75)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: right, reward: 0.542657360954
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.5426573609538659, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.54)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 74
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (7, 2), deadline = 25
0.754877344913
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7549; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: -9.97630526112
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -9.976305261117671, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.98)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: -10.7271460435
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -10.727146043526151, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -10.73)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: -10.4816811003
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -10.481681100256427, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.48)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 0.897957516238
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.8979575162382856, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.90)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: 0.755667109711
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 0.7556671097106556, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.76)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 0.685305920075
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 0.6853059200753805, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.69)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
0.950863426267
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: 2.70326599389
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 2.7032659938940298, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.70)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -9.28589675483
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -9.285896754833914, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.29)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -10.4639079364
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -10.463907936382833, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.46)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', 'right')
0.39425529456
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.54244927256
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.5424492725608954, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent drove right instead of forward. (rewarded 1.54)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: forward, reward: -9.53549999991
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': -9.535499999906682, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.54)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: left, reward: -20.7010381786
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -20.701038178594523, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.70)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: 1.95483446997
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 1.9548344699664935, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.95)
48% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 75
\-------------------------
Environment.reset(): Trial set up with start = (5, 2), destination = (1, 4), deadline = 30
0.752014254319
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7520; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: None, reward: 1.59919216786
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.5991921678623133, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.60)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: -10.728174543
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': -10.728174543012493, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.73)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
2.05755614202
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: None, reward: 2.30623232389
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.3062323238922904, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.31)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: right, reward: 1.24052121763
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 1.2405212176346483, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent drove right instead of forward. (rewarded 1.24)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: forward, reward: 1.02673526324
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 1.0267352632428817, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 1.03)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: None, reward: 2.41683707403
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': 2.4168370740288143, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.42)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: forward, reward: -9.26872785024
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': -9.268727850244188, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.27)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
1.32404576716
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: left, reward: 1.44277436675
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 1.442774366754173, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.44)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: 1.40404195866
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 8, 'action': 'left', 'reward': 1.404041958659076, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.40)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: left, reward: 0.994100966018
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 0.9941009660181404, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.99)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
1.89094959002
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: left, reward: 0.973134930211
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 10, 'action': 'left', 'reward': 0.973134930211045, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.97)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
1.43204226012
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: left, reward: 1.04119842537
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': 1.0411984253712305, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.04)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: forward, reward: 1.43711475524
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 1.4371147552415795, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.44)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 0.0997702333033
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 17, 't': 13, 'action': None, 'reward': 0.09977023330327683, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.10)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 2.05562921539
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': 2.055629215392, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.06)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 2.67190330574
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 2.6719033057410546, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.67)
47% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 76
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (8, 2), deadline = 25
0.749162022825
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7492; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'right', 'forward')
0.507181650677
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 1.41278433613
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.4127843361284673, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.41)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: -39.832829168
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -39.83282916803445, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.83)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: right, reward: 0.46851743809
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.4685174380898218, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.47)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: right, reward: 1.01289899502
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.0128989950216165, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.01)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
2.19759677135
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: forward, reward: 1.56701303341
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.5670130334129344, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.57)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: 1.78534876233
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.785348762327955, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.79)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.17566215374
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.175662153735279, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.18)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
1.64502108042
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.24666466412
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.246664664121393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.25)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: -9.71427393042
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -9.714273930424875, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.71)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: -19.4660576934
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -19.466057693443723, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.47)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: -4.07833958977
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 10, 'action': None, 'reward': -4.078339589767628, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.08)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: left, reward: 1.78771259477
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 1.7877125947675503, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.79)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: -4.2812937137
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 13, 't': 12, 'action': None, 'reward': -4.281293713695884, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.28)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: 2.72390455362
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 2.7239045536212982, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.72)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 1.33647273791
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 1.3364727379146382, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.34)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: None, reward: -4.25017935392
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 10, 't': 15, 'action': None, 'reward': -4.250179353923999, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.25)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'right')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -10.9390326537
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -10.939032653683853, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -10.94)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -40.2886487809
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': -40.2886487809226, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.29)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
2.19536232845
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: None, reward: 2.20568288013
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 7, 't': 18, 'action': None, 'reward': 2.205682880130024, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.21)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: -0.147663356423
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': -0.14766335642346473, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent drove right instead of left. (rewarded -0.15)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: left, reward: -10.7437871316
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 5, 't': 20, 'action': 'left', 'reward': -10.74378713164139, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.74)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: right, reward: 1.43266499287
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 1.432664992866872, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.43)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: 1.62376283273
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 22, 'action': None, 'reward': 1.6237628327294993, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.62)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: right, reward: -20.5628554892
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 2, 't': 23, 'action': 'right', 'reward': -20.562855489206136, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.56)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', 'forward', None)
1.66022144503
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: 0.587537139845
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 1, 't': 24, 'action': None, 'reward': 0.5875371398445588, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.59)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 77
\-------------------------
Environment.reset(): Trial set up with start = (8, 6), destination = (4, 6), deadline = 20
0.746320609243
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7463; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'forward')
1.50971479385
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.41548608674
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.4154860867437606, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.42)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.9235279568
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.92352795680202, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.92)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: -9.21916975263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -9.219169752632967, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -9.22)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
1.42483901221
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 2.86291453855
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.862914538548514, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.86)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 1.04824966699
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.048249666987185, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.05)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 0.986531917726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 0.9865319177259106, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.99)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 1.4695489759
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.4695489759027722, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.47)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', None)
2.20052260429
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 1.49354535618
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.493545356175069, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.49)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
1.84703398023
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 1.68699561663
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.6869956166303695, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.69)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: -10.9939543293
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -10.993954329257672, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -10.99)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'left', None)
2.02801498075
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 1.1823000973
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 1.1823000972964088, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.18)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 0.201343603532
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.20134360353191583, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.20)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: -4.07782156808
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': -4.077821568080612, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.08)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.05345845267
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.0534584526702115, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.05)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', None)
2.14387677538
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 0.981562452584
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.9815624525840969, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 0.98)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 0.0304355166715
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 0.030435516671462937, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.03)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 0.555315776975
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 0.5553157769746815, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 0.56)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -0.118557217625
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -0.11855721762548, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.12)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'left')
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: left, reward: 0.557872098899
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': 0.5578720988990884, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded 0.56)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, 'left')
1.6667970058
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: 1.14697726553
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 1.1469772655320822, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.15)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 78
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (5, 4), deadline = 25
0.743489972544
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7435; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: 1.29658960794
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.2965896079445907, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.30)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 2.73269983533
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 2.7326998353326677, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.73)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -39.5249986786
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -39.52499867860124, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.52)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: None, reward: 2.30716073388
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.307160733878681, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.31)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -40.1898399823
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -40.18983998225804, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.19)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: -9.6382286494
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -9.63822864940429, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.64)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: left, reward: 0.670045691696
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 0.6700456916961365, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.67)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: left, reward: -19.8902050063
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -19.890205006307966, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.89)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: 0.790978026309
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 0.7909780263086633, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.79)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: -9.05430818431
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -9.0543081843105, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.05)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None)
1.05901769548
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 2.49970174693
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 2.4997017469343774, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.50)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: -39.0509170922
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -39.050917092152226, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.05)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 0.427267570038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 0.42726757003797466, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.43)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: forward, reward: 1.50595221498
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.5059522149761824, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent drove forward instead of left. (rewarded 1.51)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: -0.266351685364
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': -0.26635168536398723, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded -0.27)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'right', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.27192663324
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.2719266332438701, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.27)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: left, reward: 1.48966578194
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 1.4896657819352257, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.49)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: 2.13167885238
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 2.1316788523816976, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.13)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
2.17877819335
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.18274500946
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.1827450094590946, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.18)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: -9.46861406642
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': -9.468614066416986, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.47)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
1.6807616014
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.47851999375
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.4785199937539197, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.48)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 2.29797901088
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 2.2979790108847897, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.30)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
1.28077562087
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: left, reward: 0.714237302388
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 0.7142373023875108, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.71)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, 'right')
1.20640203176
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: None, reward: 1.947775029
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 2, 't': 23, 'action': None, 'reward': 1.9477750290018296, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.95)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 1.27886621224
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 1.2788662122389238, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.28)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 79
\-------------------------
Environment.reset(): Trial set up with start = (5, 7), destination = (2, 4), deadline = 30
0.740670071853
Simulating trial. . .
epsilon = 0.7407; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7407; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7407; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7407; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7407; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
1.92475189226
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: right, reward: 1.79342095261
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.7934209526099958, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.79)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.15767558065
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: forward, reward: 1.66161999285
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': 1.6616199928543331, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.66)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: 1.23108283281
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': 1.231082832813586, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 1.23)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 1.67206898199
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 1.672068981994855, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.67)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: -10.5701487117
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 26, 't': 4, 'action': 'left', 'reward': -10.570148711713788, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.57)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: -4.59771890038
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': -4.597718900382482, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.60)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: 0.630098873786
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 0.6300988737860672, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.63)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 0.840641809184
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 0.8406418091838416, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent drove forward instead of right. (rewarded 0.84)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 1.14281360019
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.1428136001853786, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.14)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: left, reward: -10.1054842346
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': -10.105484234598425, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.11)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'forward', None)
1.90709666767
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: right, reward: 2.84016779549
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 2.8401677954858573, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.84)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: 2.43763559422
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 11, 'action': None, 'reward': 2.437635594219201, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.44)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
2.11922726932
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: 1.99236875731
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.9923687573139428, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.99)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', 'right')
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: right, reward: 1.20670276857
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 1.20670276857031, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent drove right instead of forward. (rewarded 1.21)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 0.215043027458
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 0.21504302745794002, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.22)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.65688467914
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 0.952946810434
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 15, 'action': 'right', 'reward': 0.9529468104341443, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.95)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'right')
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: -4.42676282117
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 14, 't': 16, 'action': None, 'reward': -4.426762821172504, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.43)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', 'right')
1.25187102306
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: right, reward: 1.45656450923
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'right'), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 1.4565645092329078, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'right')
Agent followed the waypoint right. (rewarded 1.46)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: right, reward: 1.06161567073
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 18, 'action': 'right', 'reward': 1.0616156707327382, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.06)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.32235330682
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.3223533068201414, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.32)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 0.899508875172
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 0.8995088751721185, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent drove right instead of left. (rewarded 0.90)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, 'left')
2.24449469857
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: None, reward: 1.94537942287
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 9, 't': 21, 'action': None, 'reward': 1.9453794228733456, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.95)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', 'left', None)
1.77935972121
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 1.02016202428
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 1.0201620242838239, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.02)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: -5.2560666665
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 23, 'action': None, 'reward': -5.256066666503298, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.26)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: -4.09407428762
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 6, 't': 24, 'action': None, 'reward': -4.094074287618973, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.09)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'red', 'right', 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: -20.0547842512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 5, 't': 25, 'action': 'right', 'reward': -20.054784251175832, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.05)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: forward, reward: -9.03686046116
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 26, 'action': 'forward', 'reward': -9.036860461156653, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.04)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: forward, reward: -10.8495984718
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 3, 't': 27, 'action': 'forward', 'reward': -10.849598471803747, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.85)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'green', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: -0.27911449833
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 2, 't': 28, 'action': None, 'reward': -0.2791144983304784, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded -0.28)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'green', None, None)
1.50095001131
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: right, reward: 0.246005737474
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 29, 'action': 'right', 'reward': 0.246005737473725, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.25)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 80
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (5, 6), deadline = 30
0.737860866451
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7379; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 1.51876648569
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.5187664856922845, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.52)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 2.71106381817
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 2.711063818172565, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.71)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: -10.4799171559
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': -10.479917155909666, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.48)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 1.68699507336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.6869950733561863, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.69)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None)
1.98988977346
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 1.11307292131
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 1.113072921313616, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.11)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: left, reward: 1.31836035307
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'left', 'reward': 1.3183603530665176, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 1.32)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: -9.25186294757
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': -9.251862947566298, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.25)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 2.36138550863
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 2.361385508630596, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.36)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
1.40688713567
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: left, reward: 0.933887040482
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 22, 't': 8, 'action': 'left', 'reward': 0.9338870404817257, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 0.93)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 81
\-------------------------
Environment.reset(): Trial set up with start = (3, 4), destination = (8, 7), deadline = 30
0.735062315772
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7351; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 2.45275219104
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 30, 't': 0, 'action': None, 'reward': 2.452752191036074, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.45)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: left, reward: -40.4852888297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': 'left', 'reward': -40.4852888296733, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.49)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: left, reward: -10.5972189502
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': 'left', 'reward': -10.597218950175002, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.60)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: -10.8885904704
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': -10.888590470415224, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent attempted driving forward through a red light. (rewarded -10.89)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
2.10988349473
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 1.99341148536
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.9934114853593448, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.99)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: left, reward: 2.74922712185
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'left', 'reward': 2.7492271218495175, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.75)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: left, reward: 2.01173063036
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 2.011730630358795, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.01)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 1.74190162564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 1.7419016256364652, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.74)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 2.24281799942
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.2428179994248363, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.24)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: left, reward: 1.2960845517
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 1.2960845516969428, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.30)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: -5.22240334757
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 20, 't': 10, 'action': None, 'reward': -5.222403347569918, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.22)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'right', 'forward')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: left, reward: -39.8474386735
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 19, 't': 11, 'action': 'left', 'reward': -39.84743867350612, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.85)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
2.05579801332
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.6405087585
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.6405087584964269, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.64)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'left', None)
1.84815338591
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 2.40633366582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 13, 'action': None, 'reward': 2.4063336658239667, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.41)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.92665564456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.9266556445562246, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.93)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: -10.2165060719
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': -10.21650607186982, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.22)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
1.86370557191
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: 2.34527123351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 2.3452712335143824, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.35)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 1.19868977328
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 1.1986897732789263, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.20)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 82
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (3, 3), deadline = 20
0.732274379406
Simulating trial. . .
epsilon = 0.7323; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7323; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'forward')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: right, reward: 1.48777960762
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.4877796076185281, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent drove right instead of left. (rewarded 1.49)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 0.693187874484
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 19, 't': 1, 'action': None, 'reward': 0.6931878744839907, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded 0.69)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'forward')
1.27106339625
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: 1.11833413961
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.1183341396126438, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.12)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.11702452753
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.1170245275288395, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.12)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'left', 'right')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.18142598643
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.1814259864269339, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent idled at a green light with oncoming traffic. (rewarded 1.18)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: left, reward: 1.21751139631
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.217511396313926, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.22)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: left, reward: -40.3622921118
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -40.36229211178315, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.36)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: 2.5678757608
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.5678757607979703, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.57)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: -9.24802580076
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.248025800762761, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -9.25)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 1.22925199741
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.2292519974119303, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.23)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'right', None)
0.403746400394
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: right, reward: -0.00125173230687
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': -0.00125173230686626, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded -0.00)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: forward, reward: 0.976626766886
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 0.9766267668862816, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 0.98)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.40964778675
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 2.40962405338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 2.4096240533798072, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.41)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', 'left')
1.8333312226
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: 1.44049013031
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.440490130312753, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.44)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'left')
1.40658168501
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: 0.669255378736
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.6692553787360738, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 0.67)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'right', None)
1.09553728156
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: 1.08134273903
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 1.0813427390268993, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.08)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'right', 'right')
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: left, reward: -39.8831971179
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', 'right'), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -39.88319711794907, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'right')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.88)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', 'left')
1.09396910096
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 1.20148059983
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 1.2014805998286817, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.20)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 83
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (4, 2), deadline = 30
0.729497017095
Simulating trial. . .
epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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epsilon = 0.7295; alpha = 0.5000
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/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 1.26000647843
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': 1.2600064784300988, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent drove forward instead of right. (rewarded 1.26)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: None, reward: 1.23918250945
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.239182509448169, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.24)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: -9.8394145333
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 28, 't': 2, 'action': 'left', 'reward': -9.83941453329527, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -9.84)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: None, reward: 1.92230335144
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.9223033514424024, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.92)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: 1.88853956364
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': 1.8885395636429068, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 1.89)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: -9.50777205087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': -9.507772050866382, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.51)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None)
1.71450329156
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: right, reward: 1.0777866262
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 1.0777866262022306, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.08)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: 1.74842825363
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 1.7484282536327784, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.75)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 1.19316621537
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.1931662153721336, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.19)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: -9.83678242832
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': -9.83678242831624, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.84)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'forward', 'forward')
0.950648038371
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 0.159138419653
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 0.15913841965323305, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 0.16)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: -4.56932106608
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 19, 't': 11, 'action': None, 'reward': -4.569321066077644, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.57)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 0.927739804405
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 0.9277398044048277, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.93)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.41868786223
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 1.4554387868
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 1.4554387868007321, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.46)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: right, reward: 1.6849401707
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 1.6849401707027043, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.68)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: right, reward: 1.70233688482
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'right', 'reward': 1.7023368848203089, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.70)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
1.74741095736
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: 1.80406615726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 1.8040661572616992, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.80)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: -40.9378109901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 13, 't': 17, 'action': 'left', 'reward': -40.937810990085, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.94)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: None, reward: 1.78963243342
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 18, 'action': None, 'reward': 1.7896324334196199, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.79)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 1.24156815385
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 19, 'action': 'right', 'reward': 1.2415681538513108, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.24)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: -10.0368171698
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 20, 'action': 'left', 'reward': -10.03681716980743, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.04)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 1.73966355153
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 21, 'action': None, 'reward': 1.7396635515252359, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.74)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
1.77573855731
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: left, reward: 1.8757858705
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 22, 'action': 'left', 'reward': 1.875785870501725, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.88)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: left, reward: 2.25186341729
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 7, 't': 23, 'action': 'left', 'reward': 2.251863417294794, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.25)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'left', None)
1.97462646833
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 1.52019388542
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 6, 't': 24, 'action': None, 'reward': 1.5201938854243462, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.52)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 0.668517200682
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 5, 't': 25, 'action': None, 'reward': 0.6685172006820161, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.67)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 0.448243009758
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 26, 'action': 'right', 'reward': 0.4482430097578274, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.45)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: right, reward: -0.613436544811
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 3, 't': 27, 'action': 'right', 'reward': -0.6134365448111607, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded -0.61)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: forward, reward: -10.7966018619
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 2, 't': 28, 'action': 'forward', 'reward': -10.796601861855688, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.80)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'green', 'left', 'left')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: 1.8777616863
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 1, 't': 29, 'action': 'right', 'reward': 1.8777616862974371, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.88)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 84
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (5, 5), deadline = 30
0.726730188733
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7267; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'forward')
1.39137475155
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: None, reward: 2.84541572042
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 30, 't': 0, 'action': None, 'reward': 2.8454157204218538, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.85)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
1.83891902238
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: None, reward: 2.51548545461
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.5154854546115555, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.52)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: -10.3048153209
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': -10.304815320933175, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.30)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: -40.1516676096
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': -40.15166760963682, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.15)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: left, reward: -10.5760757747
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': -10.576075774696232, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.58)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
0.817232543332
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 0.728895047907
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 0.7288950479065132, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.73)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: -20.5694826774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 24, 't': 6, 'action': 'right', 'reward': -20.56948267740844, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.57)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
1.94584287227
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.41543847087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.415438470868551, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.42)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.17688924956
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.1768892495583008, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.18)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'right', 'left')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: left, reward: -39.0534464513
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 21, 't': 9, 'action': 'left', 'reward': -39.053446451278575, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.05)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: -39.6862731147
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': -39.6862731146818, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.69)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: right, reward: 0.470337771544
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 0.4703377715436904, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.47)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: forward, reward: -9.13982144041
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': -9.139821440414288, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.14)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: -0.00423731643223
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': -0.0042373164322346835, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded -0.00)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 1.20644525186
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': 1.2064452518557263, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.21)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'forward')
1.13247963608
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 2.29408131083
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 2.294081310825982, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.29)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.06035713675
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.0603571367512314, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.06)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: 1.71442505982
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 1.7144250598207953, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.71)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 2.32783962518
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 18, 'action': None, 'reward': 2.327839625175005, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.33)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: left, reward: 1.39627827574
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 19, 'action': 'left', 'reward': 1.3962782757446635, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.40)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 2.03879419851
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 20, 'action': None, 'reward': 2.038794198509624, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.04)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
1.43706332452
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: forward, reward: 2.3952824278
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 2.3952824277992284, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.40)
27% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 85
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (8, 3), deadline = 20
0.723973854368
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7240; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.36900798013
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.3690079801252393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.37)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: -9.81737619311
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -9.817376193114104, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -9.82)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.52520223487
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 1.45247223103
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.4524722310253013, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.45)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
1.82576221391
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: 1.1993130143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.1993130143024635, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.20)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: left, reward: 0.685785411796
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 0.6857854117959773, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 0.69)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 1.02177405656
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.0217740565621087, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.02)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
1.49903030973
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.12333956414
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.1233395641411918, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.12)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: right, reward: 1.0826731364
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.0826731364026672, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.08)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: -10.7873530356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -10.78735303564768, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.79)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: -4.90721908993
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': None, 'reward': -4.907219089929632, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.91)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: 2.22597856886
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 2.2259785688551643, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.23)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: -0.0566131739921
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': -0.05661317399208721, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded -0.06)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'right', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 2.09427165269
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 2.094271652692497, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 2.09)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
1.4080607324
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 2.53082470274
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 2.5308247027410267, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.53)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'right', None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: -39.0932018244
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -39.09320182439136, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.09)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: forward, reward: -9.26847745941
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -9.268477459405117, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.27)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 0.458176402288
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 4, 't': 16, 'action': None, 'reward': 0.45817640228834877, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 0.46)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: -19.8318920864
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -19.83189208638068, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.83)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'forward')
1.19469876793
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 2.13121051249
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 2.131210512493779, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.13)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
1.86925809148
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: 1.52929630083
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 1.529296300833031, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.53)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 86
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (6, 5), deadline = 20
0.721227974198
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7212; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: -10.5880550395
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -10.58805503949818, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.59)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: -40.351577052
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -40.35157705195459, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.35)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: -9.18121096666
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -9.181210966658814, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.18)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.6663043606
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.6663043606003738, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.67)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
2.02530357079
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: 1.84005680107
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.8400568010735519, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.84)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', 'left')
1.63691067646
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 2.22153915066
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.221539150664208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 2.22)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
1.56645835841
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: left, reward: 2.56242353607
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 2.5624235360727265, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.56)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', 'forward')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: forward, reward: 1.13142243971
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.131422439712625, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 1.13)
60% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 87
\-------------------------
Environment.reset(): Trial set up with start = (4, 3), destination = (7, 4), deadline = 20
0.718492508572
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7185; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: None, reward: 2.80428625043
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.80428625042547, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.80)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: left, reward: -10.7238187576
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.723818757564603, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.72)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: forward, reward: -10.5597328308
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.559732830804206, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.56)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: 0.401183060771
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.40118306077087673, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.40)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 1.21717499451
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.217174994510771, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.22)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.82799179398
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 1.90383803651
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.9038380365114713, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.90)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: forward, reward: -9.14882729785
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -9.148827297849936, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.15)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'right')
1.79637504786
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: 1.87486626635
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.8748662663535876, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.87)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'left')
0.968910248173
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.75133719079
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.7513371907874802, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.75)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', 'right')
0.78709916977
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 0.215335280641
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.21533528064084373, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent drove right instead of forward. (rewarded 0.22)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'left', None)
2.06444094724
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: 1.05437513202
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 1.0543751320227035, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.05)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
1.93268018593
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 1.12410910082
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.1241091008172823, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.12)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, 'right')
0.307478733708
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.18330057264
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.1833005726377284, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded 1.18)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
1.86591491525
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.34178595865
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.3417859586511427, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.34)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 0.823123748949
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.8231237489491797, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 0.82)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 0.258185173265
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.2581851732651337, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove right instead of left. (rewarded 0.26)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: forward, reward: 0.401027725027
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.40102772502679485, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 0.40)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'right', 'left')
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: 1.01363675916
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 1.0136367591564648, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent drove forward instead of right. (rewarded 1.01)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: 1.10608881903
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 1.1060888190263725, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 1.11)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 0.391659023322
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 0.39165902332241265, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 0.39)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 88
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (5, 7), deadline = 30
0.715767417991
Simulating trial. . .
epsilon = 0.7158; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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epsilon = 0.7158; alpha = 0.5000
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Simulating trial. . .
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/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', 'left')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: -4.51213927007
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 30, 't': 0, 'action': None, 'reward': -4.512139270067168, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.51)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: -5.9214918604
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 29, 't': 1, 'action': None, 'reward': -5.921491860396539, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.92)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: 0.371188712028
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 28, 't': 2, 'action': 'left', 'reward': 0.37118871202774695, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 0.37)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'left', 'left')
1.70196411439
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 1.5410140027
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 1.5410140026988437, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.54)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'forward')
1.90960908766
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.81670372837
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.816703728372209, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.82)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 0.220733531273
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 5, 'action': 'left', 'reward': 0.2207335312729345, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.22)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None)
1.39614495888
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 2.92278221277
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 2.9227822127722067, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.92)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: -5.77848240455
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': None, 'reward': -5.7784824045473915, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.78)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'right', None)
1.08844001029
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.96591345722
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': 1.9659134572195913, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.97)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: left, reward: 1.40790230045
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 1.4079023004481463, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.41)
67% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 89
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (1, 6), deadline = 25
0.713052663104
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
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epsilon = 0.7131; alpha = 0.5000
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epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7131; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', None)
2.12423979345
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: None, reward: 1.88577019486
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.8857701948597352, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.89)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
2.00500499416
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: None, reward: 2.05688441636
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.0568844163601474, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.06)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', 'right')
1.12140899971
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: None, reward: 2.88294475506
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.8829447550632663, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.88)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: None, reward: 1.03189639337
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.0318963933724572, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.03)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
1.55940803963
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: left, reward: 1.88326514113
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.8832651411311319, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.88)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.75142306897
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.7514230689692478, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.75)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 1.59819984082
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.598199840815366, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.60)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', None)
2.1772022385
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.44773783972
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.4477378397194316, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.45)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 2.1584841744
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.1584841743996313, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.16)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: -0.0698621364765
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': -0.0698621364764529, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded -0.07)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: -9.11225861178
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': -9.112258611783504, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.11)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: -5.15364361336
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 14, 't': 11, 'action': None, 'reward': -5.153643613361879, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.15)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: 2.69384754318
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 2.693847543176659, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.69)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
1.60385043695
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 2.39962952252
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.39962952251818, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.40)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: forward, reward: -10.3821248644
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': -10.38212486437699, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.38)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: -20.2300452686
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -20.230045268606347, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.23)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: 0.324729248195
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 0.32472924819546767, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.32)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: -39.9804232184
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': -39.980423218396496, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.98)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
2.00173997973
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 0.729452084127
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 0.7294520841265513, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.73)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: -9.69838700484
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': -9.698387004837533, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.70)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, 'left')
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 1.13719620588
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.1371962058776592, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded 1.14)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
1.91617287616
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: forward, reward: 2.11166028246
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 2.1116602824557154, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.11)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: 0.81196216019
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.8119621601895743, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 0.81)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: left, reward: -0.62897875472
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -0.6289787547202828, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded -0.63)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', 'right', 'left')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: right, reward: 0.48032823322
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.48032823322001517, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'right', 'left')
Agent followed the waypoint right. (rewarded 0.48)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 90
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (7, 5), deadline = 25
0.710348204709
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7103; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: -4.82636373143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': -4.826363731433815, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.83)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'right', 'forward')
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -20.6471767932
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -20.647176793164416, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.65)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 2.67301084106
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 2.673010841064421, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.67)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: -9.05068525808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 22, 't': 3, 'action': 'left', 'reward': -9.050685258081563, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -9.05)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 1.39995740798
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.399957407980388, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.40)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: -9.70483797533
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -9.704837975328132, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.70)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: -9.71918304912
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': -9.719183049124483, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.72)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: -39.0836145855
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': -39.08361458546387, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.08)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -10.1511799893
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -10.151179989347384, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.15)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: -4.81740158338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 9, 'action': None, 'reward': -4.817401583377624, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.82)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: left, reward: 0.255452464221
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 0.2554524642211419, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 0.26)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: 0.193027890315
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 0.19302789031497503, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 0.19)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
1.96944271757
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 1.40201013637
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.4020101363742687, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.40)
48% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 91
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (7, 5), deadline = 20
0.707654003755
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7077; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.95704127739
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.9570412773931647, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.96)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: -10.9726949199
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -10.972694919904592, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.97)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -9.4049027507
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -9.404902750697794, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.40)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.63411310716
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.60230839798
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.6023083979757753, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.60)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.61821075257
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.5158539182
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.51585391820252, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.52)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
2.01391657931
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 1.70528656246
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.705286562458869, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.71)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
1.85960157088
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 2.29736782945
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.2973678294507236, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.30)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'forward')
2.16798274064
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 1.32929175345
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.3292917534510498, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.33)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: 0.874224059922
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 0.8742240599220296, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.87)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 0.708624146989
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 0.7086241469887027, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.71)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: right, reward: -0.0575399815362
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': -0.05753998153618778, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded -0.06)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: left, reward: -39.0168946836
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -39.016894683626376, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.02)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: -10.2025393125
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -10.202539312544324, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.20)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: right, reward: 2.19602316843
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 2.196023168433418, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.20)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: left, reward: -10.6973753484
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -10.69737534835626, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.70)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.6430774594
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 0.486348064924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.4863480649243497, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.49)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: -9.42579225285
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -9.425792252852167, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.43)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: forward, reward: -0.651817599221
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -0.6518175992210701, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded -0.65)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'left')
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 0.54062453917
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.5406245391695195, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 0.54)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
2.06703233539
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 0.39787401652
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.3978740165199879, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.40)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 92
\-------------------------
Environment.reset(): Trial set up with start = (1, 4), destination = (3, 7), deadline = 25
0.704970021337
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7050; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: -5.63067247051
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': -5.630672470506623, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.63)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: 1.03268040527
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 1.0326804052712104, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.03)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 1.28862760004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.2886276000373575, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.29)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: -5.41953439781
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 22, 't': 3, 'action': None, 'reward': -5.419534397807896, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.42)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: -39.896635442
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': -39.89663544198968, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.90)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: -10.3472007481
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -10.347200748121173, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.35)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 1.63130577038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.631305770381076, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.63)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: forward, reward: 1.23665149271
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.2366514927125747, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 1.24)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
1.15348752937
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 1.51447195967
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.514471959673928, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.51)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: right, reward: 1.5625757142
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.5625757141998171, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.56)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: -9.73745594075
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -9.737455940750204, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.74)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 1.62285136834
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.622851368336642, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.62)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: -9.47523891082
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': -9.475238910824983, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -9.48)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: -5.212907002
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': None, 'reward': -5.212907002004785, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.21)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'right')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: -5.43493015375
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 11, 't': 14, 'action': None, 'reward': -5.434930153754632, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.43)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: left, reward: 1.95835788851
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 1.9583578885126152, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.96)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: left, reward: 1.16724032492
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 1.1672403249208765, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 1.17)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: -4.20306945084
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 8, 't': 17, 'action': None, 'reward': -4.203069450836901, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.20)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: right, reward: 1.39867986661
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.398679866613887, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.40)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: left, reward: -39.1920744713
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': -39.192074471280236, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.19)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: -10.4058997964
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': -10.405899796383927, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.41)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: None, reward: 1.45716604545
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 4, 't': 21, 'action': None, 'reward': 1.4571660454527087, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.46)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: -0.0970655979566
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': -0.09706559795661018, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded -0.10)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: right, reward: 1.33905797774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.3390579777400886, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.34)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 0.764464366016
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': 0.764464366016492, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'left', 'forward')
Agent drove forward instead of right. (rewarded 0.76)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 93
\-------------------------
Environment.reset(): Trial set up with start = (1, 4), destination = (5, 6), deadline = 30
0.702296218699
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
Simulating trial. . .
epsilon = 0.7023; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: -40.3070932196
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': -40.30709321956399, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.31)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'right', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: -10.049009101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': -10.049009101006378, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -10.05)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 2.50255423055
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 2.502554230547947, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.50)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'forward', 'forward')
1.08376816392
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 1.45051678148
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 1.450516781480237, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 1.45)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 2.14631169681
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.146311696814717, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.15)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
2.07848470017
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: 1.34663759645
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 1.3466375964462787, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.35)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: -10.8356548714
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': -10.835654871379502, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.84)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.71256114831
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: forward, reward: 2.57791964824
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 2.577919648244543, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.58)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', 'right')
0.473895917825
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: right, reward: 0.645264547091
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 0.6452645470911845, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent drove right instead of left. (rewarded 0.65)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.74741017688
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 2.84319456057
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.8431945605742186, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.84)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.53169480905
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.5316948090465237, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.53)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: left, reward: 0.675091740519
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 19, 't': 11, 'action': 'left', 'reward': 0.6750917405190903, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent drove left instead of forward. (rewarded 0.68)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', None)
1.39916082848
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: right, reward: 1.8468454908
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 1.8468454907994643, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.85)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'right', 'left')
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: forward, reward: -10.2672868314
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': -10.267286831381133, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left')
Agent attempted driving forward through a red light. (rewarded -10.27)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, 'left')
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: None, reward: 2.31201064116
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 16, 't': 14, 'action': None, 'reward': 2.3120106411648207, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.31)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, 'right')
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: left, reward: -9.63702954762
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -9.637029547617434, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -9.64)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: 0.778319394679
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 0.7783193946789937, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.78)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'right')
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 1.65377002116
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 13, 't': 17, 'action': None, 'reward': 1.653770021157688, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.65)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: -9.74955779388
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': -9.749557793878814, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.75)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: left, reward: 0.474730638912
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 19, 'action': 'left', 'reward': 0.474730638911817, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.47)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: -4.63101749656
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 20, 'action': None, 'reward': -4.631017496555607, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.63)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: left, reward: -0.309751624351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 21, 'action': 'left', 'reward': -0.30975162435120407, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.31)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: -5.42865061535
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 22, 'action': None, 'reward': -5.4286506153459335, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.43)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: -5.54266496389
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 7, 't': 23, 'action': None, 'reward': -5.542664963893295, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.54)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'green', None, 'left')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: -0.297147248512
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 6, 't': 24, 'action': 'right', 'reward': -0.29714724851242336, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded -0.30)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: -20.1194409263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 5, 't': 25, 'action': 'right', 'reward': -20.119440926298715, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.12)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: -9.11202895124
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 26, 'action': 'left', 'reward': -9.112028951243278, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.11)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.76744278672
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 3, 't': 27, 'action': None, 'reward': 1.767442786724652, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.77)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: -9.10437709535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 2, 't': 28, 'action': 'left', 'reward': -9.104377095348536, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.10)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'green', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: 0.469038734085
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 1, 't': 29, 'action': 'forward', 'reward': 0.4690387340853295, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 0.47)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 94
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (5, 5), deadline = 30
0.69963255723
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6996; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 0.000131319396468
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 0.0001313193964675996, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.00)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 0.0314193002458
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 0.03141930024583617, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 0.03)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
1.83294956561
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 1.58556587391
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 1.5855658739050633, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.59)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
1.09679437837
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 0.909925154938
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 0.9099251549379083, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 0.91)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
1.53142054932
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 2.55017539109
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.550175391092041, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.55)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: forward, reward: -9.47894551681
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': -9.478945516810892, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.48)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
1.72133659038
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: 1.89473416762
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 1.8947341676172764, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.89)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
1.71603187027
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.07435169606
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.0743516960589887, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.07)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.81495408647
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.814954086472484, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.81)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -10.4029898002
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': -10.402989800200023, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.40)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
1.68938243638
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.32093736433
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.3209373643266256, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.32)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: -5.2723032221
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 11, 'action': None, 'reward': -5.2723032220984765, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.27)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 1.04689623107
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 1.046896231073459, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.05)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', 'right')
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 2.33655149888
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 2.336551498884476, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 2.34)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', None)
1.33397974452
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 1.0052544829
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 1.0052544828951293, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.01)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
1.51908934574
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: 1.64551793911
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 15, 'action': 'right', 'reward': 1.6455179391109613, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.65)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: forward, reward: 0.681339854658
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 0.6813398546582761, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.68)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: 1.597294314
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 1.5972943139962992, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.60)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 1.70289243492
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 12, 't': 18, 'action': 'right', 'reward': 1.7028924349236163, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.70)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', None)
1.91349858889
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 2.07688444951
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.0768844495086323, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.08)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 1.01054919842
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 1.0105491984215818, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.01)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'right', None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: left, reward: -19.6024788357
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 9, 't': 21, 'action': 'left', 'reward': -19.602478835744318, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.60)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'right', 'forward')
0.743889803809
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: right, reward: 0.0269113672289
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 0.02691136722887144, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent drove right instead of left. (rewarded 0.03)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: None, reward: 0.530444168232
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 7, 't': 23, 'action': None, 'reward': 0.5304441682323977, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 0.53)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: 1.09382413932
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 24, 'action': 'right', 'reward': 1.093824139317988, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.09)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 1.42024447369
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 25, 'action': 'right', 'reward': 1.4202444736875492, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.42)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('forward', 'red', None, 'left')
1.39519178316
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 0.905182614223
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 4, 't': 26, 'action': None, 'reward': 0.9051826142229324, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.91)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 1.92588370313
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 3, 't': 27, 'action': None, 'reward': 1.9258837031321347, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.93)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: -0.347434746837
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 2, 't': 28, 'action': 'right', 'reward': -0.34743474683677034, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent drove right instead of forward. (rewarded -0.35)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: None, reward: -0.532418150141
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 1, 't': 29, 'action': None, 'reward': -0.5324181501408376, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded -0.53)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 95
\-------------------------
Environment.reset(): Trial set up with start = (2, 6), destination = (7, 5), deadline = 20
0.696978998467
Simulating trial. . .
epsilon = 0.6970; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'right', 'right')
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: forward, reward: -10.5274913369
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -10.527491336890796, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent attempted driving forward through a red light. (rewarded -10.53)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: right, reward: 0.72754656007
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.7275465600699652, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent drove right instead of forward. (rewarded 0.73)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: -5.32014273692
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': -5.320142736916059, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.32)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: forward, reward: 0.613116511213
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 0.6131165112126923, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.61)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: 0.994062288068
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 0.9940622880678454, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.99)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: -10.0351785731
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': -10.035178573084801, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.04)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: -9.06320870552
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -9.063208705518223, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.06)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
1.9951915192
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.19794442707
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.197944427070206, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.20)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: -10.6644263081
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -10.664426308119443, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -10.66)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
1.52839464337
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 0.813911695319
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 0.8139116953189431, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.81)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
1.36268272536
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.04246022743
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.042460227434894, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.04)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: -20.1893521711
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -20.189352171119676, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.19)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: -0.245552647205
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -0.24555264720457948, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded -0.25)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'right', None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 1.69080826548
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.6908082654819745, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.69)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 96
\-------------------------
Environment.reset(): Trial set up with start = (5, 7), destination = (2, 6), deadline = 20
0.694335504093
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6943; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: -39.9467379547
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -39.94673795470884, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.95)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
1.58741337459
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 2.27274645362
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.2727464536226503, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.27)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: -19.1702261916
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': -19.170226191621698, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.17)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'forward', None)
0.967646706414
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: right, reward: 1.02508699437
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.0250869943686074, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.03)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'left', None)
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: left, reward: -9.13029632519
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -9.130296325194458, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.13)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: right, reward: 2.06804469525
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.0680446952462153, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.07)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: left, reward: -9.06442699878
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -9.064426998784764, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.06)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: None, reward: 0.219497829405
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 0.2194978294048564, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.22)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
1.59606831467
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: 1.81871994242
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.8187199424204505, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.82)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: 0.974481418129
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 0.9744814181289121, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.97)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'left')
2.20347385094
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 1.10972282495
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.1097228249491964, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.11)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: -10.9925922007
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -10.992592200683017, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.99)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'right', None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: -10.4351099894
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -10.435109989387508, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -10.44)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: 0.251577563102
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.2515775631024738, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.25)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'forward')
1.72576484098
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: right, reward: 0.691610243541
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.6916102435410338, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 0.69)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'right')
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 1.12947417901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.129474179010326, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.13)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: -10.7744714694
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -10.77447146942126, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.77)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
1.808035379
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: left, reward: 0.571092906819
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 0.5710929068194655, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.57)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: left, reward: -10.1325945515
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -10.13259455149075, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.13)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: left, reward: -9.33873705054
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -9.338737050543518, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.34)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 97
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (4, 2), deadline = 20
0.691702035936
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6917; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: None, reward: 2.84587949339
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.8458794933933147, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.85)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'left')
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: left, reward: -10.0554600869
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.055460086901794, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.06)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: -10.0527380369
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.05273803693815, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.05)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: None, reward: 1.14373618361
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.1437361836073836, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.14)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: -9.3636260311
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -9.363626031097347, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.36)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
1.9408747977
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: right, reward: 2.65977032724
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.6597703272422435, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.66)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: 1.7232975557
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 1.723297555695305, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.72)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'forward', 'forward')
0.797281424477
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 0.913101605486
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 13, 't': 7, 'action': None, 'reward': 0.9131016054858896, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 0.91)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
1.16961711371
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: right, reward: 1.9428186971
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.94281869709527, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.94)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 1.17688667115
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.1768866711518802, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.18)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 1.64889996085
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.6488999608495936, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.65)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: 0.854392432695
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 0.8543924326947059, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.85)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, 'right')
1.50187382169
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 2.12754023149
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.127540231487061, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.13)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
1.5588904669
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: left, reward: 2.03962153305
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 2.0396215330477356, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.04)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'left')
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: 0.688183692687
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.6881836926870782, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 0.69)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: 0.50185562657
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.5018556265699461, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.50)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: -0.0138997838536
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -0.01389978385360724, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.01)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
1.18956414291
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: left, reward: 1.78798797133
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 1.7879879713338775, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.79)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: left, reward: -10.1306131401
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -10.130613140068613, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.13)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: -4.29192405988
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': -4.2919240598786645, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.29)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 98
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (1, 4), deadline = 20
0.689078555968
Simulating trial. . .
epsilon = 0.6891; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6891; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6891; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6891; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
1.82796411859
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: 2.75248922211
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 2.7524892221093857, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.75)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: -9.00538529307
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -9.005385293066912, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.01)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: -10.5583697753
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.558369775321598, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.56)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.14047208626
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.1404720862582187, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.14)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 0.199381106997
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.1993811069972059, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.20)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: forward, reward: -9.64633567937
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -9.646335679369255, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.65)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.36559603193
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 2.20622979377
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.2062297937711284, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.21)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: 1.03885354613
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.038853546127833, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.04)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: -9.41687582438
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.416875824378803, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.42)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 2.04202516038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.042025160378576, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.04)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
1.93007991411
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.61011909036
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.610119090360593, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.61)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', 'left')
0.776356852828
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: 0.922199777057
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 0.9221997770574653, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 0.92)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: -9.62278927324
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -9.6227892732419, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.62)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', 'right')
0.874062830078
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 0.216135115132
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.21613511513178618, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove right instead of left. (rewarded 0.22)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 0.667024522215
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.6670245222151215, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.67)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: right, reward: 1.19833502514
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.1983350251447027, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.20)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
1.56475109672
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: right, reward: 1.87068032376
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.8706803237588403, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.87)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'left')
1.03791853187
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 0.564128478412
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 0.5641284784124154, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 0.56)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: -0.501222959489
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': -0.5012229594885829, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded -0.50)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', None)
2.0407979702
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 0.548232742772
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.548232742771501, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.55)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 99
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (1, 4), deadline = 20
0.686465026307
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6865; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', 'right')
0.949347339536
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: right, reward: 0.587628076215
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 0.5876280762150199, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent drove right instead of forward. (rewarded 0.59)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: None, reward: 1.56558976238
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.5655897623849107, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.57)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: forward, reward: -9.99026118344
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -9.990261183441088, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -9.99)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
1.48877605712
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: left, reward: 1.82655694225
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.826556942254809, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.83)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: 1.96171281223
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.9617128122322418, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.96)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 1.32512549969
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.3251254996940172, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.33)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.78591291285
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: None, reward: 2.70451314364
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.704513143635339, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.70)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'left')
1.71112525268
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: 2.70526542071
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.7052654207131788, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.71)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
1.53803545091
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 2.28225858804
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.2822585880388973, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.28)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.36355508071
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.3635550807094452, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.36)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: -39.7505647037
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -39.75056470367309, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.75)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: 2.03228236563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 2.032282365627986, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.03)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 0.623680373526
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.6236803735264809, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.62)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'right')
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: -0.148416417301
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 7, 't': 13, 'action': 'right', 'reward': -0.14841641730088406, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove right instead of left. (rewarded -0.15)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 2.01049143165
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 2.010491431649891, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.01)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: 1.27830580858
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.2783058085815489, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.28)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', None)
1.55148134739
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: 0.498002918923
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.4980029189230182, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 0.50)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'forward')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: forward, reward: 0.740674297288
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 0.7406742972880458, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.74)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 100
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (4, 5), deadline = 25
0.683861409212
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6839; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', None)
1.77009950223
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.95667640979
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.9566764097947127, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.96)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
2.36338795601
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.51254092483
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.5125409248341914, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.51)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: -10.1450443566
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -10.145044356551825, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.15)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', 'right')
0.559580232458
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 0.100891688675
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.1008916886750002, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent drove right instead of left. (rewarded 0.10)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: -39.8721965351
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': -39.87219653512271, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.87)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
1.58230364242
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 0.658153433224
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.6581534332242313, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.66)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: -39.3597982849
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -39.35979828486655, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.36)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'forward')
1.93138566951
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.22158170078
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.221581700782385, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.22)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 1.0155034293
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.015503429299272, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.02)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
1.86983824709
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 2.45119095179
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.4511909517886616, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.45)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
1.59656797313
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 2.10561795689
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.1056179568919875, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.11)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 1.83948754935
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.8394875493503642, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.84)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 1.07215793329
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.0721579332856384, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.07)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: forward, reward: -9.36917127295
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': -9.369171272954793, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.37)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: None, reward: 2.21109858227
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 14, 'action': None, 'reward': 2.2110985822697913, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.21)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: None, reward: -4.29943887766
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': -4.299438877660826, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.30)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: left, reward: 1.93519533477
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 1.9351953347658213, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.94)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: left, reward: -19.4977525209
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': -19.49775252086923, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.50)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', 'left', 'left')
1.03364420306
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 1.07293011188
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': 1.07293011187987, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.07)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, 'left')
1.40575872267
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: 2.12706493096
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 2.1270649309577543, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.13)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', 'forward', None)
1.02474213316
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: 0.449326225485
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': 0.44932622548544177, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 0.45)
16% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 101
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (3, 3), deadline = 20
0.681267667089
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6813; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: -39.1878334969
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -39.187833496934864, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.19)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: -10.5495389424
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -10.54953894235353, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.55)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: -10.4033946119
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.403394611880245, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -10.40)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.84529025718
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 2.36693895693
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.3669389569322723, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.37)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
2.10611460706
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 2.24254113023
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.242541130232604, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.24)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: left, reward: 1.07640787979
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.0764078797918069, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 1.08)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 1.41959119842
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.4195911984171414, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.42)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
1.91014701948
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 1.2080505466
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.208050546595905, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.21)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.50515990036
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 1.40504207242
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.405042072423637, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.41)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
2.16051459944
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 2.6367954563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 2.636795456298938, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.64)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'right', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: None, reward: 0.79700937093
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 0.7970093709300108, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.80)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
1.56865345433
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 1.90446108497
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.9044610849676102, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.90)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 2.28826543005
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.2882654300476117, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.29)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
2.39865502787
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: forward, reward: 1.71189349165
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.7118934916498418, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.71)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: -5.61233007981
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 6, 't': 14, 'action': None, 'reward': -5.612330079809444, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.61)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: 1.04677528084
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 1.0467752808421111, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.05)
20% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 102
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (8, 6), deadline = 25
0.678683762482
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6787; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', None)
1.62300315964
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 1.00330113316
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.0033011331614126, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.00)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: 0.675558034667
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 0.6755580346671517, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.68)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', 'right', 'forward')
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 1.52310889046
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.5231088904649088, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent followed the waypoint right. (rewarded 1.52)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.87168320822
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.33866048918
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.3386604891833218, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.34)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.5510247703
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 2.94564872031
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.9456487203126924, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.95)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'right', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 2.89267022944
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 2.8926702294398687, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 2.89)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', 'forward')
0.747532000057
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 0.323044482833
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 0.32304448283328757, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent drove right instead of forward. (rewarded 0.32)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -9.23168880444
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -9.23168880444301, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.23)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 0.689978378557
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 0.6899783785566623, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.69)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
2.30032256247
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 2.15848865099
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 2.1584886509905425, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.16)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
2.22940560673
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.42551859401
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.4255185940079484, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.43)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'forward')
2.08905524724
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.05488694818
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.0548869481759482, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.05)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: -0.212055591422
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': -0.21205559142155184, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.21)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: 2.44966955182
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 2.449669551817381, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.45)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 0.251209677876
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.2512096778764009, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.25)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'left')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: 1.42283141869
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 1.422831418689031, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.42)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
2.22815580526
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 1.39007509367
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.390075093665711, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.39)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: -4.13250433579
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 17, 'action': None, 'reward': -4.132504335794362, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.13)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, 'forward')
2.36994811108
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: 0.98657022443
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 7, 't': 18, 'action': 'left', 'reward': 0.9865702244303871, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 0.99)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: -10.3008837246
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': -10.300883724578961, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.30)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
1.6051718487
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 2.18140653101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': 2.181406531006929, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.18)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
2.24833674531
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 1.5488580965
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 1.5488580964989824, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.55)
12% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 103
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (6, 2), deadline = 20
0.67610965808
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6761; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'left')
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: -5.97613110548
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': -5.976131105484955, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.98)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 0.938895557052
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.9388955570524126, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 0.94)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 1.97310903129
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.9731090312871682, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.97)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: left, reward: -9.78762366528
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -9.787623665284558, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.79)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.8985974209
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 1.46358517514
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.463585175137309, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.46)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: -4.96913773694
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': -4.969137736938213, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.97)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: right, reward: -0.00582089251074
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': -0.005820892510740583, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded -0.01)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: forward, reward: -39.7468104302
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': -39.74681043023786, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.75)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: None, reward: 1.65414355273
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.6541435527312252, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.65)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: 1.28991979476
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.2899197947620968, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.29)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'forward')
1.9626004403
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 1.66984319959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.6698431995862966, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.67)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
1.73655726965
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 1.99063158222
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.9906315822216178, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.99)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'right', None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: forward, reward: -9.26498673886
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -9.264986738863684, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.26)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: -20.6238339761
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -20.623833976112866, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.62)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: -0.188773176374
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -0.1887731763742585, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded -0.19)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'forward')
1.20868754226
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 1.28209435247
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.282094352470259, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.28)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'left', None)
2.17432786864
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.64743112732
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.647431127315534, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.65)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: 1.2800435263
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 1.2800435263034353, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.28)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: -9.98616162263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -9.98616162262627, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -9.99)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, None)
1.68109129802
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 1.23243268753
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': 1.232432687531223, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.23)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 104
\-------------------------
Environment.reset(): Trial set up with start = (5, 3), destination = (2, 7), deadline = 25
0.673545316714
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6735; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
1.89328918985
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.00063193531
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.0006319353085975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.00)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
1.44696056258
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.91925808698
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.9192580869822713, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.92)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.68310932478
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 2.021372386
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.021372385996087, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.02)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 1.73141310187
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.7314131018680778, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.73)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'left')
1.81551337769
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: left, reward: 1.8844696172
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.8844696172026605, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.88)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: None, reward: -4.48507540704
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 20, 't': 5, 'action': None, 'reward': -4.485075407037579, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.49)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
1.45676199278
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: 1.32510792923
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.325107929228997, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.33)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: -39.1422053134
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': -39.142205313390356, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.14)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.85224085539
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.01312833028
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.0131283302789964, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.01)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: -9.04708342275
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': -9.047083422749331, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.05)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 0.406905805461
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 0.4069058054612157, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.41)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: 0.870815008329
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 0.8708150083293004, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.87)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, 'left')
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 2.39647694249
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 2.3964769424893166, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.40)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', None)
2.37363223158
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 2.71717468176
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 2.717174681761494, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.72)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
1.43268459283
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 1.00178560038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.0017856003843655, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.00)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 1.78875050832
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.7887505083159254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.79)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: -0.363805442942
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': -0.36380544294233064, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded -0.36)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, 'right')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: 1.39164004351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 1.3916400435090215, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.39)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: -10.9850856871
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -10.985085687083433, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.99)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: -4.56898856813
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': -4.568988568131445, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.57)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, None)
1.86722566737
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: 2.36932694984
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 5, 't': 20, 'action': 'left', 'reward': 2.3693269498446003, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.37)
16% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 105
\-------------------------
Environment.reset(): Trial set up with start = (8, 5), destination = (2, 3), deadline = 20
0.670990701353
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6710; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.86359442593
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 2.48155855812
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.4815585581202466, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.48)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'forward')
1.81622181994
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: 1.77993651359
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.7799365135901881, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.78)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: -9.82256715734
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -9.822567157335858, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.82)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'right', 'right')
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: -39.8751733882
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', 'right'), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -39.87517338819294, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'right')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.88)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: 1.6116923924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.6116923924035482, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.61)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: 1.3736552975
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.373655297497817, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.37)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: left, reward: 0.463089642874
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 0.4630896428738751, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.46)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: 1.10189222512
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.1018922251167325, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent drove forward instead of right. (rewarded 1.10)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
1.5562179054
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 1.91043375282
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.9104337528188047, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.91)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
1.82746210037
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 0.851287152621
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.8512871526207058, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.85)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.390934961
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 2.49113551288
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 2.4911355128789907, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.49)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 1.4567075925
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.4567075925049282, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.46)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: -10.7401081413
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -10.740108141283628, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.74)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.69887141472
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 2.02801000952
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 2.028010009519525, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.03)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 106
\-------------------------
Environment.reset(): Trial set up with start = (4, 3), destination = (3, 6), deadline = 20
0.668445775111
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6684; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: None, reward: -5.30605806547
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': -5.306058065467187, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.31)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: left, reward: 1.7451796733
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 1.7451796733038258, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.75)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: left, reward: -9.59636485093
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -9.59636485092533, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.60)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 2.45605283072
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.4560528307196954, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.46)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: forward, reward: -39.9627022492
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -39.96270224921828, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.96)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'right', None)
1.8643689086
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 1.65772637336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.6577263733643628, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.66)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: left, reward: 2.83222418528
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 2.832224185279947, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.83)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: -4.37880734644
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': -4.378807346438832, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.38)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: -4.61884721271
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': -4.618847212709969, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.62)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'right', None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: 2.32215980959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.3221598095921294, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.32)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'right', None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: 2.58549512126
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.585495121259064, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.59)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: 1.00905916931
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.0090591693135544, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 1.01)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'right', None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: -5.39105391055
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 8, 't': 12, 'action': None, 'reward': -5.391053910546525, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.39)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: -10.6432479928
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -10.643247992849231, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.64)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: -9.55685293933
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -9.556852939326033, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.56)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: left, reward: 0.676027044963
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 0.6760270449633345, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.68)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: -0.180926767824
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': -0.18092676782369588, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded -0.18)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 1.23937963907
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.2393796390679286, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.24)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: forward, reward: -10.3552483256
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': -10.355248325625556, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.36)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 0.753504331225
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.7535043312250763, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.75)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 107
\-------------------------
Environment.reset(): Trial set up with start = (1, 4), destination = (6, 7), deadline = 30
0.665910501236
Simulating trial. . .
epsilon = 0.6659; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: -10.6239618302
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': -10.623961830160036, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.62)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: -39.2282967478
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': -39.22829674780233, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.23)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', 'right')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 1.22005827841
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.2200582784098901, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.22)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: -9.43431889165
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': -9.434318891652197, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.43)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: -9.71593803355
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': -9.715938033551232, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.72)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 1.34151344048
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.3415134404834745, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.34)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 1.19852205598
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 1.198522055980961, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.20)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'right', None)
1.52717673376
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 1.03747126403
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.0374712640337669, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.04)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
1.42152178133
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 1.29052258564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.2905225856386555, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.29)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: left, reward: -20.9717149852
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': -20.971714985233668, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.97)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: left, reward: 1.19926520922
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 20, 't': 10, 'action': 'left', 'reward': 1.1992652092202258, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.20)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'left', None)
1.3131521464
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 2.07756475761
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 2.077564757610747, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.08)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 0.480888097871
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 0.48088809787126674, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent drove right instead of left. (rewarded 0.48)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
0.737034179321
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: forward, reward: 0.840285257011
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 0.8402852570109933, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 0.84)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
1.86344071212
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: forward, reward: 1.17446094751
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': 1.1744609475089196, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.17)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
1.51895082982
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: forward, reward: 0.830998054591
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 0.8309980545908096, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.83)
47% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 108
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (7, 6), deadline = 20
0.663384843121
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6634; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: 2.76877261591
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 2.7687726159125527, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.77)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', None)
1.29762590641
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: left, reward: 0.879799900973
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 0.8797999009731247, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.88)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
1.26894834124
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 2.09631753773
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.0963175377284324, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.10)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.4118120741
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.411812074095476, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.41)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
1.88391712879
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.65898561011
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.6589856101096956, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.66)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.16270060235
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.1627006023478428, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.16)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', 'right')
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 1.82074880304
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.8207488030396946, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent drove right instead of forward. (rewarded 1.82)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', None)
1.46048619372
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: 1.51943258769
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.5194325876896182, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.52)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'left')
1.65659833795
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.13632466809
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.1363246680934465, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.14)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'left', None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 1.66990362902
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.6699036290186362, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.67)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 109
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (8, 2), deadline = 25
0.660868764295
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6609; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'right')
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: 1.48714067756
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 1.4871406775627893, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent followed the waypoint forward. (rewarded 1.49)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: None, reward: 2.39974638087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.399746380869815, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.40)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'right')
0.768487707875
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 0.563433198956
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.5634331989556765, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent drove right instead of forward. (rewarded 0.56)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: -4.33006179497
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': -4.330061794971915, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.33)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
2.11827630861
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: 2.50794321882
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 2.507943218822163, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.51)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: -9.92238688556
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -9.92238688555509, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.92)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'right', 'left')
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: 1.70551889005
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.705518890053365, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent followed the waypoint right. (rewarded 1.71)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 0.384236587368
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 0.38423658736765565, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent drove right instead of forward. (rewarded 0.38)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: left, reward: 1.68735898414
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 1.6873589841361751, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.69)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: forward, reward: 1.13052336357
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.1305233635748166, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 1.13)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'right', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 2.65402587354
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.654025873542372, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.65)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: -10.0464686732
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -10.046468673226157, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.05)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 1.33786908983
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.337869089834145, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.34)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: 1.51938204275
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 1.5193820427526836, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 1.52)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', 'forward')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: -39.9412667352
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -39.941266735193096, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.94)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: 1.36185176841
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': 1.3618517684087041, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.36)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: 2.09456782223
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 2.0945678222260966, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.09)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: None, reward: -4.12003561265
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 17, 'action': None, 'reward': -4.120035612645192, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.12)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, None)
1.1749744422
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 1.16435146914
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': 1.1643514691393544, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.16)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, 'forward')
1.57648368515
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.35195127171
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.3519512717100453, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.35)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: -0.417272196948
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': -0.41727219694770157, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded -0.42)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
1.22559834782
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: forward, reward: 2.22448375349
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 2.2244837534924544, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.22)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: left, reward: -0.299691064522
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': -0.2996910645219064, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded -0.30)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: left, reward: 0.727550209075
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 23, 'action': 'left', 'reward': 0.7275502090754526, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.73)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
2.13258414009
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: 1.37652658203
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 24, 'action': None, 'reward': 1.376526582034317, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.38)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 110
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (3, 6), deadline = 35
0.658362228425
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6584; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'right')
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: right, reward: 2.66863406527
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 35, 't': 0, 'action': 'right', 'reward': 2.668634065273947, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.67)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
1.38136815869
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 1.22920606731
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 34, 't': 1, 'action': 'forward', 'reward': 1.2292060673147487, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.23)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
1.16966295567
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 2.12115833305
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 33, 't': 2, 'action': 'forward', 'reward': 2.1211583330546784, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.12)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.76543153438
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 32, 't': 3, 'action': None, 'reward': 2.765431534379528, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.77)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: -10.1869207973
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 31, 't': 4, 'action': 'forward', 'reward': -10.186920797325287, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.19)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: -10.3873579195
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 30, 't': 5, 'action': 'left', 'reward': -10.387357919527823, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.39)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 1.65612607639
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 29, 't': 6, 'action': 'right', 'reward': 1.6561260763925487, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.66)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'left')
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: left, reward: 1.57724235698
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 28, 't': 7, 'action': 'left', 'reward': 1.5772423569750187, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.58)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'left')
1.89646150302
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 2.23572934043
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 27, 't': 8, 'action': None, 'reward': 2.235729340429421, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.24)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: right, reward: 1.45005970958
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 26, 't': 9, 'action': 'right', 'reward': 1.450059709581762, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.45)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'forward')
1.22697738537
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: forward, reward: 0.897335871083
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 25, 't': 10, 'action': 'forward', 'reward': 0.8973358710827559, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.90)
69% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 111
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (4, 6), deadline = 20
0.655865199317
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
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epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6559; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: left, reward: 2.09212529891
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 2.0921252989113075, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 2.09)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'right', None)
1.41995799921
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.33973594027
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.3397359402688072, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.34)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: -19.3512238871
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': -19.351223887051233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.35)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', 'right')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: left, reward: -9.47927592756
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -9.47927592755747, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent attempted driving left through a red light. (rewarded -9.48)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 1.0799126839
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.0799126839032631, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 1.08)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'right', None)
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 1.48075365063
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.4807536506297683, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.48)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
1.56634632453
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 1.73326465223
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.7332646522314157, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.73)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: left, reward: -10.2384791648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -10.238479164806318, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.24)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'left')
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.16411885528
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.1641188552832498, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.16)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: 1.7004316185
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.7004316185015025, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 1.70)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: -10.6720511859
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -10.672051185851576, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.67)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: -9.47933824354
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -9.479338243538564, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.48)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
1.95136959167
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: None, reward: 1.61782553286
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.6178255328593278, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.62)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: 2.19162939377
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 2.1916293937684035, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 2.19)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: -5.50717909668
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': None, 'reward': -5.507179096676825, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.51)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: right, reward: 0.385673967171
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.3856739671710949, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 0.39)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: forward, reward: 0.560124441326
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.5601244413264304, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.56)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 0.626749037171
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 3, 't': 17, 'action': None, 'reward': 0.6267490371711801, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 0.63)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', 'forward')
1.10520403712
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 1.84845583829
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.848455838291081, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.85)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: forward, reward: -10.995344934
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': -10.995344934034033, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -11.00)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 112
\-------------------------
Environment.reset(): Trial set up with start = (5, 2), destination = (1, 2), deadline = 20
0.653377640914
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6534; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', 'left')
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: right, reward: 1.99057769673
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.9905776967252486, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'left')
Agent drove right instead of left. (rewarded 1.99)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: 2.92397888662
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 2.9239788866218444, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.92)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: 0.515752110397
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': 0.5157521103967521, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 0.52)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None)
1.64980548838
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 1.8492192588
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.8492192588023448, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.85)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'forward')
1.06215662823
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: forward, reward: 1.52490882791
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.5249088279117995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.52)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, 'forward')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: 2.43530373402
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.4353037340235497, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.44)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 113
\-------------------------
Environment.reset(): Trial set up with start = (2, 4), destination = (6, 3), deadline = 25
0.650899517295
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6509; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: forward, reward: 0.833628942562
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 0.8336289425619028, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 0.83)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'left', 'left')
1.04606264946
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: 2.69739000784
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 2.6973900078428668, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 2.70)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: -4.79839172753
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': -4.798391727526741, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.80)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: 1.94149253864
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': 1.941492538636556, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.94)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: -9.66819538497
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -9.66819538496606, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.67)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: -4.87738456396
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': -4.877384563961677, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.88)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: left, reward: 1.14587403889
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 1.145874038893278, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.15)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: left, reward: -19.0834357915
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -19.0834357914679, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.08)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: 0.153826201727
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 0.15382620172684003, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.15)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'left')
2.08144438465
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 2.07327466438
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 2.073274664381964, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.07)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: -19.9636677768
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -19.963667776750377, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.96)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
0.894108677807
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: -0.105429405454
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': -0.10542940545372537, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded -0.11)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: left, reward: -9.50511702701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -9.505117027005344, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -9.51)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: 1.42850436143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.4285043614262205, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 1.43)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', 'forward')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 1.5221063665
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.522106366501166, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.52)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'right', None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 1.14630690963
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.1463069096280512, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.15)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: forward, reward: -0.281769856441
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': -0.2817698564408734, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded -0.28)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: left, reward: 0.86294112765
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 0.8629411276503762, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.86)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', None)
1.91087949798
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 0.631040600355
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 7, 't': 18, 'action': None, 'reward': 0.6310406003546696, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.63)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 0.41792967346
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 0.4179296734596444, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.42)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'right', None)
1.81504727902
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.1096826887
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 2.1096826887030824, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.11)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', 'left', None)
1.43005255944
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 0.518389901617
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 4, 't': 21, 'action': None, 'reward': 0.5183899016171956, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.52)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.07344507459
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 1.0734450745850206, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent drove right instead of left. (rewarded 1.07)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'right', 'right')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: -19.0927170463
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': 'right'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'right', 'right'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -19.092717046311993, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'right')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.09)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: -4.23401287358
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 24, 'action': None, 'reward': -4.23401287357815, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.23)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 114
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (6, 4), deadline = 35
0.648430792677
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
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epsilon = 0.6484; alpha = 0.5000
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epsilon = 0.6484; alpha = 0.5000
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epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
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epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
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epsilon = 0.6484; alpha = 0.5000
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epsilon = 0.6484; alpha = 0.5000
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epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
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epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
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epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6484; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: None, reward: 1.81157619092
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 35, 't': 0, 'action': None, 'reward': 1.8115761909242194, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.81)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 2.18620011265
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 34, 't': 1, 'action': 'right', 'reward': 2.18620011265491, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.19)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -10.1085493174
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 33, 't': 2, 'action': 'left', 'reward': -10.108549317435925, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.11)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: 0.117286560956
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 32, 't': 3, 'action': 'right', 'reward': 0.11728656095561463, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent drove right instead of forward. (rewarded 0.12)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
1.75455536106
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 2.18121608422
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 31, 't': 4, 'action': None, 'reward': 2.18121608422144, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.18)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: forward, reward: -9.57350764231
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 30, 't': 5, 'action': 'forward', 'reward': -9.573507642309174, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.57)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.53338996031
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: 1.64129343178
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 29, 't': 6, 'action': 'left', 'reward': 1.6412934317771424, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.64)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: -10.2351806656
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 28, 't': 7, 'action': 'forward', 'reward': -10.235180665569624, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.24)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -39.550859062
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 27, 't': 8, 'action': 'left', 'reward': -39.55085906196414, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.55)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: -10.4936872744
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 26, 't': 9, 'action': 'forward', 'reward': -10.493687274401877, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.49)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 1.86343496704
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 10, 'action': 'right', 'reward': 1.8634349670421964, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.86)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 1.93257560939
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 11, 'action': None, 'reward': 1.9325756093898976, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.93)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: left, reward: -9.84475661189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 23, 't': 12, 'action': 'left', 'reward': -9.844756611889233, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.84)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: -4.78630960585
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 22, 't': 13, 'action': None, 'reward': -4.786309605845538, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.79)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'right', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: left, reward: -20.0213655738
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 21, 't': 14, 'action': 'left', 'reward': -20.021365573845326, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.02)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: -4.36255831093
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 20, 't': 15, 'action': None, 'reward': -4.362558310931714, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.36)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
1.95023066602
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 1.70560944383
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 16, 'action': None, 'reward': 1.7056094438334133, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.71)
51% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'right', None)
1.96236498386
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 0.907705064263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 18, 't': 17, 'action': None, 'reward': 0.9077050642628262, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.91)
49% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 1.04032658241
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 17, 't': 18, 'action': None, 'reward': 1.0403265824102752, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.04)
46% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 0.263491417253
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 16, 't': 19, 'action': None, 'reward': 0.263491417252545, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 0.26)
43% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, 'left')
1.71361692721
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: 2.52324564997
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 15, 't': 20, 'action': 'left', 'reward': 2.523245649974797, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.52)
40% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', None, None)
1.78459756226
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.94307440456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 21, 'action': None, 'reward': 1.9430744045593322, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.94)
37% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 0.928958012551
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 22, 'action': None, 'reward': 0.9289580125507939, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.93)
34% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: -39.4125052022
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 12, 't': 23, 'action': 'forward', 'reward': -39.41250520222867, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.41)
31% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', None, 'forward')
1.57197109771
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.84406636774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 11, 't': 24, 'action': None, 'reward': 1.8440663677385314, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.84)
29% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: right, reward: 0.357186886833
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 25, 'action': 'right', 'reward': 0.3571868868331989, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.36)
26% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', 'left', None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: left, reward: 1.2985012821
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 9, 't': 26, 'action': 'left', 'reward': 1.298501282097066, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.30)
23% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 115
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (2, 2), deadline = 35
0.645971431411
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6460; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: forward, reward: -39.4359851027
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 35, 't': 0, 'action': 'forward', 'reward': -39.43598510270023, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.44)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
1.73332582911
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 1.36748715764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 34, 't': 1, 'action': 'right', 'reward': 1.3674871576380547, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.37)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 2.10933336864
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 33, 't': 2, 'action': 'forward', 'reward': 2.1093333686383993, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.11)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: -10.5017839288
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 32, 't': 3, 'action': 'forward', 'reward': -10.501783928788013, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.50)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: right, reward: 1.29185457154
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 31, 't': 4, 'action': 'right', 'reward': 1.2918545715423735, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.29)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: None, reward: -4.98800203405
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 30, 't': 5, 'action': None, 'reward': -4.988002034051296, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.99)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: left, reward: -40.9730830758
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 29, 't': 6, 'action': 'left', 'reward': -40.973083075822494, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.97)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: right, reward: 0.695542808401
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 7, 'action': 'right', 'reward': 0.6955428084011775, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.70)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 0.428881668923
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 27, 't': 8, 'action': None, 'reward': 0.4288816689227458, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.43)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: 0.827623748837
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 26, 't': 9, 'action': 'forward', 'reward': 0.8276237488365912, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.83)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'right')
1.61363035031
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 2.41313079421
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 25, 't': 10, 'action': 'left', 'reward': 2.413130794209325, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.41)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'forward')
1.25227203042
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 0.946252079527
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 24, 't': 11, 'action': None, 'reward': 0.9462520795268747, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.95)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
1.82792005492
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 2.02168121509
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 12, 'action': None, 'reward': 2.021681215085076, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.02)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'right', 'left')
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: right, reward: 0.707619712592
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 22, 't': 13, 'action': 'right', 'reward': 0.7076197125919185, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove right instead of left. (rewarded 0.71)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'right', 'left')
0.461104930806
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.40389325211
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 21, 't': 14, 'action': None, 'reward': 1.4038932521122265, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent properly idled at a red light. (rewarded 1.40)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'forward', None)
0.788659718166
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 1.67537821451
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 20, 't': 15, 'action': 'forward', 'reward': 1.6753782145119296, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.68)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 1.31260929609
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 19, 't': 16, 'action': None, 'reward': 1.31260929609397, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.31)
51% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'right', None)
1.2823239989
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: forward, reward: 2.00541804129
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 18, 't': 17, 'action': 'forward', 'reward': 2.005418041285492, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 2.01)
49% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: -4.0365336498
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 18, 'action': None, 'reward': -4.036533649802664, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.04)
46% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, 'forward')
1.70801873272
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 1.35180483862
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 16, 't': 19, 'action': None, 'reward': 1.3518048386201087, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.35)
43% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: right, reward: 1.36762720067
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 15, 't': 20, 'action': 'right', 'reward': 1.367627200667903, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded 1.37)
40% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', None, 'left')
1.24732099892
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 2.25382044434
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 14, 't': 21, 'action': None, 'reward': 2.253820444340399, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.25)
37% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: forward, reward: -9.83884013844
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 22, 'action': 'forward', 'reward': -9.838840138440865, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.84)
34% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', None, None)
1.924800635
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 1.635049206
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 23, 'action': None, 'reward': 1.635049206002167, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.64)
31% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 2.13638214969
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 24, 'action': None, 'reward': 2.1363821496850464, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.14)
29% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'green', None, None)
1.58734169604
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: left, reward: 2.03741164301
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 25, 'action': 'left', 'reward': 2.0374116430066627, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.04)
26% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'red', None, None)
1.95815353509
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: 0.497630109386
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 26, 'action': None, 'reward': 0.49763010938574403, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.50)
23% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: right, reward: 0.951661000595
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 27, 'action': 'right', 'reward': 0.9516610005950793, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.95)
20% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: -5.54962841452
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 7, 't': 28, 'action': None, 'reward': -5.549628414517656, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.55)
17% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: left, reward: -40.455196044
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 6, 't': 29, 'action': 'left', 'reward': -40.455196044048954, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.46)
14% of time remaining to reach destination.
/-------------------
| Step 30 Results
\-------------------
Environment.step(): t = 30
('right', 'red', None, 'right')
1.61542927577
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: 1.3251055323
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 5, 't': 30, 'action': None, 'reward': 1.325105532295841, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.33)
11% of time remaining to reach destination.
/-------------------
| Step 31 Results
\-------------------
Environment.step(): t = 31
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: -10.8714572961
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 31, 'action': 'forward', 'reward': -10.871457296094597, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.87)
9% of time remaining to reach destination.
/-------------------
| Step 32 Results
\-------------------
Environment.step(): t = 32
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: 0.661788039799
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 3, 't': 32, 'action': None, 'reward': 0.6617880397989748, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.66)
6% of time remaining to reach destination.
/-------------------
| Step 33 Results
\-------------------
Environment.step(): t = 33
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 1.57510976697
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 2, 't': 33, 'action': 'right', 'reward': 1.575109766967558, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.58)
3% of time remaining to reach destination.
/-------------------
| Step 34 Results
\-------------------
Environment.step(): t = 34
('right', 'red', None, 'right')
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: left, reward: -10.9802252866
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 1, 't': 34, 'action': 'left', 'reward': -10.980225286632976, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -10.98)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 116
\-------------------------
Environment.reset(): Trial set up with start = (3, 7), destination = (6, 6), deadline = 20
0.643521397983
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6435; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: -5.11516504805
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': -5.115165048052289, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.12)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 1.24285848516
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.2428584851607476, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 1.24)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: left, reward: -9.72681024642
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -9.726810246421687, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.73)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
0.974221230527
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 2.64859772602
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.6485977260187026, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.65)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 0.832578471579
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 0.8325784715786281, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.83)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', 'right')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: 1.04119538054
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.0411953805424359, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 1.04)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'right', 'forward')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: -20.8023866159
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': -20.802386615888352, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.80)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 1.31371449966
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.3137144996581336, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.31)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
1.27096004917
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: 1.13154660329
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.131546603294384, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.13)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.20125332623
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: 2.43617987958
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.436179879580556, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.44)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
1.81871660291
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: 1.09996927349
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.0999692734878113, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.10)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: left, reward: -0.117229439147
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -0.11722943914712469, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded -0.12)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, 'left')
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: -4.17018796243
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 8, 't': 12, 'action': None, 'reward': -4.1701879624330696, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.17)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, 'left')
1.6151071385
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 0.993999509324
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.9939995093237677, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.99)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
2.17257649203
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 2.27130340611
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 2.2713034061107544, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.27)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: -0.0989573410764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': -0.09895734107637089, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded -0.10)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'left')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: -9.74660432552
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -9.746604325522561, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.75)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 0.936410202766
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.9364102027656157, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.94)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: -39.3180789605
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': -39.318078960480676, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.32)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: -9.2926441661
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': -9.292644166096116, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -9.29)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 117
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (3, 6), deadline = 20
0.641080657016
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
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epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
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epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
Simulating trial. . .
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Simulating trial. . .
epsilon = 0.6411; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', 'left')
1.36012371948
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.31014799862
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.310147998618801, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.31)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'forward')
1.5174113983
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.53517770607
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.5351777060657432, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.54)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: -10.6387674027
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.63876740270009, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.64)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.4593429382
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 2.28268463827
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.282684638270889, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.28)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 1.02531163577
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.0253116357742393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.03)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: 2.45337220077
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 2.4533722007700263, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.45)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
1.23201896634
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 1.91670529212
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.916705292117449, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.92)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: left, reward: -10.7415102467
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -10.741510246712053, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.74)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
1.87101378823
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.85507626814
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.855076268135199, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.86)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: left, reward: 1.56701099996
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.5670109999602113, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent drove left instead of forward. (rewarded 1.57)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: None, reward: -5.09893798675
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': -5.098937986746604, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.10)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: 1.44128562896
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 1.4412856289623996, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.44)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
1.74951237359
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 2.22918255581
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 2.2291825558123164, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.23)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'left', 'left')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: forward, reward: -10.1264648947
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -10.126464894714392, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent attempted driving forward through a red light. (rewarded -10.13)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.12864389421
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.128643894212034, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.13)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: -9.66280093703
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -9.662800937028, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.66)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'left', None)
1.94077928233
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 1.9405753636
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.9405753635959524, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.94)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.137315262
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.1373152620043678, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.14)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
1.26685612999
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.51885190494
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.5188519049370943, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.52)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: 0.855453643152
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': 0.8554536431518469, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.86)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 118
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (5, 4), deadline = 20
0.638649173264
Simulating trial. . .
epsilon = 0.6386; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: forward, reward: -9.4107557211
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -9.410755721097695, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.41)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'right', None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 1.24159749597
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.2415974959706662, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.24)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, 'right')
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 1.14147129888
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.1414712988801268, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.14)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: forward, reward: -10.5370637712
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.537063771212754, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.54)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
1.9893474647
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: 1.88454479165
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.884544791646042, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.88)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: left, reward: -9.54200606618
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': -9.542006066179638, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.54)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: -10.9760147181
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -10.976014718087828, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.98)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: 2.06411681802
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.064116818023365, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.06)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'right', 'left')
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: left, reward: -40.3510334152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -40.35103341520728, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.35)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: right, reward: 1.73741519983
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.7374151998317298, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.74)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'left')
1.76610390716
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: None, reward: 1.8449255202
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.8449255201995176, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.84)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'right')
1.369360272
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: forward, reward: 2.31911835486
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 2.3191183548561827, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 2.32)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 119
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (3, 4), deadline = 30
0.636226911617
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6362; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: -9.07420384519
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 30, 't': 0, 'action': 'left', 'reward': -9.074203845190748, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -9.07)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
1.81140947827
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 2.56440599936
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.564405999363621, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.56)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.40061143855
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.4006114385524466, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.40)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', 'right')
1.46022703777
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.10725694185
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.1072569418470106, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.11)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 1.35517321914
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 1.355173219136641, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.36)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
2.22193994907
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 1.78775326574
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 1.7877532657387047, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.79)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 1.56870585564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 1.568705855637992, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.57)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.61087372115
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.6108737211508148, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.61)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
1.79425958869
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 2.02931723455
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.029317234549597, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.03)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None)
1.91178841162
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 2.15897807104
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.158978071042336, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.16)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: -10.6897574138
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': -10.689757413752943, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.69)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 1.38942295272
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 1.3894229527165, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent drove right instead of left. (rewarded 1.39)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: -9.41683492045
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': -9.416834920445222, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.42)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.26802418885
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 13, 'action': None, 'reward': 1.2680241888510277, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.27)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, 'right')
1.81470702659
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.99103484516
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.9910348451622752, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.99)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
1.24795800555
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 0.860167037131
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 15, 'action': None, 'reward': 0.8601670371313508, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.86)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'right', 'left')
0.407919226086
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 1.70424003985
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 1.7042400398474138, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove right instead of left. (rewarded 1.70)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'right', None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: forward, reward: 1.62240587475
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 1.6224058747528278, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent drove forward instead of right. (rewarded 1.62)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'left', 'forward')
0.896259395919
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: forward, reward: 0.176789737953
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': 0.17678973795298392, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent drove forward instead of right. (rewarded 0.18)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, 'right')
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: None, reward: 2.17054000514
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.170540005137504, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.17)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', None, None)
2.0048466074
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: right, reward: 1.85117963371
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 1.8511796337071855, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.85)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: right, reward: 1.49980903423
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 1.4998090342283787, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.50)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: left, reward: 2.19451985505
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 22, 'action': 'left', 'reward': 2.194519855049995, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.19)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: left, reward: -9.38759213523
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 7, 't': 23, 'action': 'left', 'reward': -9.387592135226882, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.39)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: left, reward: -9.01938495011
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 6, 't': 24, 'action': 'left', 'reward': -9.01938495011082, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.02)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: -40.040172153
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 25, 'action': 'forward', 'reward': -40.04017215302296, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.04)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('forward', 'green', 'left', None)
1.72504105066
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 1.34035006282
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 26, 'action': 'forward', 'reward': 1.340350062819131, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.34)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 0.764179393295
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 3, 't': 27, 'action': 'forward', 'reward': 0.7641793932952592, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.76)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'red', 'forward', None)
2.43796444042
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: None, reward: 1.08977708274
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 2, 't': 28, 'action': None, 'reward': 1.0897770827446724, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.09)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('left', 'green', 'forward', None)
0.996366850391
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: -0.600626654996
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 1, 't': 29, 'action': 'right', 'reward': -0.6006266549964234, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded -0.60)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 120
\-------------------------
Environment.reset(): Trial set up with start = (4, 3), destination = (3, 6), deadline = 20
0.633813837099
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6338; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.92801312056
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: 1.91488269485
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.9148826948474194, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.91)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 1.97647659135
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.9764765913461046, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.98)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 1.58971826964
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.589718269644658, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.59)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: 0.45716787058
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.45716787057993014, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.46)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'right', 'forward')
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: -20.1585976432
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': -20.158597643184706, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.16)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'right', None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: right, reward: 1.15194399562
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.151943995622284, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 1.15)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'left')
2.07735952452
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: 1.90012147203
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.900121472029784, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.90)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: -19.5327262022
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': -19.532726202208874, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.53)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'forward')
1.79807916677
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 2.23816783482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.2381678348183094, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.24)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: left, reward: -39.8200665059
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -39.820066505877705, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.82)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
1.837180664
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 1.67335512357
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.6733551235738706, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.67)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 0.797043509197
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.7970435091972448, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.80)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.25043214376
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: 1.64957225224
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 1.6495722522391172, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.65)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
1.39285401746
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 2.41896743999
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 2.4189674399917207, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.42)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.47609027011
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.476090270106827, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.48)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.71330313754
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.7133031375409686, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.71)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
1.450002198
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: forward, reward: 1.54401104086
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 1.5440110408558436, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.54)
15% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 121
\-------------------------
Environment.reset(): Trial set up with start = (4, 5), destination = (1, 6), deadline = 20
0.631409914863
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6314; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, None)
1.49700661943
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: forward, reward: 2.165157748
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 2.1651577479992343, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.17)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: 0.844887188146
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.8448871881457215, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.84)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 2.76086308668
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.7608630866762747, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.76)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: left, reward: -10.4711205319
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -10.471120531866568, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.47)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: forward, reward: -10.7250924867
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.725092486693484, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.73)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
1.3942303364
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: left, reward: 2.34653477953
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 2.34653477952679, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.35)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: left, reward: -9.87275284505
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -9.872752845051993, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.87)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
1.7170759859
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 1.14100995089
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.1410099508944878, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.14)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: right, reward: 0.510501019943
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 0.5105010199430567, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.51)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: 0.846088265874
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 0.8460882658737678, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.85)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'right', 'forward')
0.820765665482
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 0.959472013367
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 0.9594720133672463, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 0.96)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: forward, reward: -10.9548840575
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -10.95488405753482, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.95)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: -10.6146129538
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -10.614612953767121, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.61)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 0.377912800736
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.3779128007356217, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 0.38)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -10.6345117665
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -10.63451176649098, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.63)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 2.09345889792
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 2.0934588979186914, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.09)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -10.9308200712
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -10.930820071212427, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -10.93)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', None)
2.06025661182
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 0.422341768604
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.42234176860366346, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 0.42)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', None)
1.57910839433
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.47157362702
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.4715736270198707, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.47)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: -20.061385389
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 1, 't': 19, 'action': 'right', 'reward': -20.061385388970987, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.06)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 122
\-------------------------
Environment.reset(): Trial set up with start = (3, 7), destination = (8, 5), deadline = 25
0.629015110197
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6290; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 1.7422006419
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.742200641902198, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.74)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 0.436520628564
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.43652062856397, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 0.44)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.90746280401
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 2.05470754162
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.0547075416214415, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.05)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: -5.18123759137
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': -5.181237591374218, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.18)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: -5.0602707803
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 21, 't': 4, 'action': None, 'reward': -5.060270780300456, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.06)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.98108517281
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 1.69872675701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.6987267570060551, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.70)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'right', None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 2.40158746814
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.401587468139266, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.40)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'right')
2.01338057226
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: 2.82767044649
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 2.827670446485577, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.83)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
1.86304502818
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 2.16761829927
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.167618299274645, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.17)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
2.01533166373
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 2.05525686423
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.055256864225772, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.06)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
2.03529426398
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 0.866779696064
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 0.8667796960644503, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.87)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: 0.976876247741
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 0.9768762477409224, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent drove left instead of forward. (rewarded 0.98)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: forward, reward: -40.0228557373
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': -40.022855737262645, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.02)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None)
1.89182888478
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 2.57033113915
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 2.570331139145389, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.57)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 2.56721436053
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 2.5672143605280358, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.57)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: -9.73495211553
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -9.734952115530245, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.73)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: -19.7057382143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -19.70573821426172, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.71)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', 'forward')
0.615541416407
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: forward, reward: 0.866784163927
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 0.8667841639269527, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 0.87)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 123
\-------------------------
Environment.reset(): Trial set up with start = (4, 5), destination = (7, 3), deadline = 25
0.62662938852
Simulating trial. . .
epsilon = 0.6266; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6266; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.9214479077
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 2.95122567037
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 2.9512256703737156, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.95)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
1.02885606068
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: forward, reward: 2.94157683508
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 2.9415768350812797, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.94)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: right, reward: 1.22096765701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.2209676570065688, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.22)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: left, reward: 1.7249047022
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': 1.724904702196041, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.72)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: 0.652063509423
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 0.6520635094226778, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded 0.65)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'forward', None)
1.55040649337
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 1.30046219451
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.300462194513518, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.30)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: -20.919300812
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': -20.919300812048604, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.92)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: right, reward: 2.81724591598
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 2.8172459159814114, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.82)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.42395516587
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.423955165870222, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.42)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 0.93517160843
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.9351716084300166, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.94)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: 1.80034848173
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.8003484817349056, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.80)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'forward')
2.01812350079
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.56605305606
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.5660530560595718, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.57)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.23228832639
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.2322883263906714, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.23)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'left', None)
1.95857695715
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 1.01305350982
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 1.013053509816711, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.01)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'forward')
1.84034734069
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: right, reward: 2.47204244468
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 2.4720424446817124, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.47)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 0.503972598803
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.5039725988026824, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.50)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
2.43633678904
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: 2.00803827098
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 2.008038270981101, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.01)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', 'forward')
1.35673870047
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 1.14694551556
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 1.1469455155608628, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 1.15)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', 'right')
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 0.963907765847
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 7, 't': 18, 'action': None, 'reward': 0.9639077658468509, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 0.96)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'forward', None)
1.90425677537
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: right, reward: 1.84268740726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.842687407264354, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.84)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 0.274011413403
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': 0.2740114134028293, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.27)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, 'right')
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: 1.71590374246
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 1.715903742458532, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.72)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: -40.803432624
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 3, 't': 22, 'action': 'left', 'reward': -40.80343262399882, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.80)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', 'forward', None)
1.76387076158
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 0.642698903202
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.6426989032022303, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.64)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'forward', None)
1.20328483239
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 0.334100052144
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 1, 't': 24, 'action': None, 'reward': 0.33410005214376826, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.33)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 124
\-------------------------
Environment.reset(): Trial set up with start = (4, 4), destination = (2, 7), deadline = 25
0.624252715382
Simulating trial. . .
epsilon = 0.6243; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6243; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6243; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6243; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
1.61489870384
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: 1.70395416117
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.7039541611653726, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.70)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
2.11198318422
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: 1.91093714629
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.9109371462917546, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.91)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: -5.73512511077
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': -5.735125110773568, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.74)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: left, reward: 1.28333912081
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': 1.2833391208140212, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.28)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 1.37184771189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.3718477118887105, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.37)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: -10.8369990787
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -10.83699907872225, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.84)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: -10.962645386
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -10.962645385975799, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.96)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', 'forward')
1.53814913863
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: 2.24087889359
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 2.240878893590432, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.24)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'left')
1.75057072163
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.47571697939
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.475716979390766, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.48)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 0.760293368439
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.7602933684391335, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.76)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -20.4907835418
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -20.49078354177525, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.49)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: -10.2357098922
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -10.235709892238198, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.24)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 0.519532087655
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 0.5195320876546872, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.52)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 2.045962387
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.0459623870042094, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.05)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
2.22218753001
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: right, reward: 2.61162635428
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 2.611626354283195, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.61)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'left', 'right')
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: left, reward: 1.55752882369
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 1.5575288236922427, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent drove left instead of forward. (rewarded 1.56)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: left, reward: -10.4253547549
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -10.425354754852673, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.43)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'left', 'right')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: 0.999554134135
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 0.9995541341346255, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent drove left instead of right. (rewarded 1.00)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: 0.765200102186
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 0.765200102186061, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.77)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
1.76154608639
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 1.32582532758
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.325825327583714, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.33)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: forward, reward: -40.255306358
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': -40.25530635802924, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.26)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 1.95643930951
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 1.95643930951036, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.96)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
1.83108218371
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 0.426940488889
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.4269404888894939, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.43)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'right', None)
0.841000492337
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 0.127154159832
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 0.12715415983152312, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.13)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, 'forward')
2.15619489269
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 0.514336080491
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.5143360804908701, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 0.51)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 125
\-------------------------
Environment.reset(): Trial set up with start = (1, 6), destination = (5, 5), deadline = 25
0.621885056465
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6219; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: 1.49166727193
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 1.4916672719282122, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 1.49)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, 'left')
2.11314385051
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: None, reward: 1.0379071123
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.037907112301157, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.04)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: None, reward: 0.983572947567
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 0.9835729475672588, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.98)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'right')
1.90287093588
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: None, reward: 2.31858503332
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.318585033318894, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.32)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: 2.58388137627
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 2.5838813762721804, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.58)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
1.70215181848
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.21943779753
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.2194377975337314, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.22)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: -10.5841156665
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -10.584115666520667, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.58)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', 'left')
1.55870198775
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 1.49797736655
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.4979773665451217, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.50)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'forward', 'right')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: right, reward: 0.779331332348
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 0.7793313323480685, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent drove right instead of forward. (rewarded 0.78)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: forward, reward: 1.0321632409
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.0321632408997559, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.03)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: left, reward: -20.7193930424
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -20.719393042424105, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.72)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
2.12165390439
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: left, reward: 1.51765240016
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 1.5176524001645926, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.52)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
1.4290429684
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 1.31950003683
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.3195000368278393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.32)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', 'right')
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: 0.810708528639
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 0.8107085286393463, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent followed the waypoint forward. (rewarded 0.81)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: -9.48655605843
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': -9.48655605842572, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.49)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
2.03538324133
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.30408179922
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.3040817992244016, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.30)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', 'left')
1.87172632865
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: left, reward: 2.60003423741
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 2.600034237413938, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 2.60)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
1.1290113363
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: forward, reward: 1.24380788714
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.2438078871362086, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.24)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 126
\-------------------------
Environment.reset(): Trial set up with start = (3, 6), destination = (7, 6), deadline = 20
0.619526377579
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6195; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'forward')
0.778474665369
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: None, reward: 0.381463972865
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 0.3814639728646998, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded 0.38)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'left', 'forward')
0.699339933307
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 2.05493258213
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.0549325821261593, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 2.05)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: forward, reward: 1.02930533733
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.029305337325042, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 1.03)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, 'forward')
1.33526548659
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 1.94170683878
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.9417068387833358, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.94)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 0.536470577354
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.5364705773541881, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.54)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
0.768692442269
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 1.4993169039
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.4993169039044814, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.50)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: -5.46686886868
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 14, 't': 6, 'action': None, 'reward': -5.466868868680937, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.47)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.81965315228
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: 2.78005206568
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.7800520656780487, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.78)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: -10.5007250972
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -10.500725097187175, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.50)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: 0.779094039941
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.779094039941435, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.78)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'right', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 0.50731206721
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.5073120672102193, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.51)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: 2.54315188691
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.5431518869107466, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.54)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, 'forward')
1.79208827843
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: None, reward: 1.6823676535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.68236765350289, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.68)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'forward', None)
1.52534101067
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: None, reward: 0.838337409065
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.8383374090651046, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.84)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', None)
1.42543434394
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 0.706709656528
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.706709656527758, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 0.71)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', 'forward')
1.11657554693
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 0.723944830447
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.7239448304471061, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 0.72)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: -0.453046924778
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': -0.45304692477819786, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded -0.45)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: left, reward: 1.93381106785
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 1.9338110678505311, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.93)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: -0.0998122959356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': -0.09981229593555718, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.10)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
2.29985260898
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: 0.752063622764
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 0.7520636227637307, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.75)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 127
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (5, 5), deadline = 25
0.617176644664
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6172; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', 'right')
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: left, reward: 1.45779114986
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.4577911498592393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent drove left instead of forward. (rewarded 1.46)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'left')
1.44456306512
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 1.33623239959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.3362323995857397, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.34)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.46079480801
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 1.7021227393
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.7021227392957035, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.70)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'right')
0.745389653173
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 0.29230613202
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.2923061320204696, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded 0.29)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: left, reward: 2.50955378007
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 2.509553780071327, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.51)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', 'forward')
0.920260188689
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 2.84130742921
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.8413074292081686, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.84)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', 'left')
0.389273316576
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: right, reward: 1.67646392321
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.676463923209563, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent drove right instead of forward. (rewarded 1.68)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'left')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 1.67857932493
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.6785793249302838, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded 1.68)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
1.13400467309
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 1.55747101398
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.5574710139796295, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.56)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: right, reward: 0.436875851012
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.4368758510120032, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.44)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: forward, reward: 0.813912666833
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 0.8139126668329885, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.81)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: 1.29176658284
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.2917665828390437, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.29)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: right, reward: 0.858302994653
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 0.8583029946533955, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent drove right instead of forward. (rewarded 0.86)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: forward, reward: 0.476421672453
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 0.4764216724530168, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.48)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', 'left')
1.66909725686
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: None, reward: 1.66716517443
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.6671651744308726, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.67)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 0.00487706100495
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.0048770610049539975, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.00)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'forward', None)
1.34573784353
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 1.66277021537
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.6627702153694013, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.66)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: -0.111874501671
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 8, 't': 17, 'action': None, 'reward': -0.11187450167126944, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded -0.11)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, 'forward')
1.95543743799
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: left, reward: 2.52071370204
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 7, 't': 18, 'action': 'left', 'reward': 2.520713702039182, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.52)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: left, reward: -10.2787462812
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': -10.278746281163475, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.28)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: right, reward: 0.55092019954
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 0.5509201995400657, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.55)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: -40.3536366164
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': -40.353636616384364, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.35)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: -39.8681888022
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': -39.868188802200066, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.87)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, 'left')
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 1.01654912424
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.0165491242449605, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.02)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, None)
2.28299009191
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: right, reward: 1.47519281986
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 1.4751928198554722, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.48)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 128
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (3, 4), deadline = 20
0.614835823791
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6148; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: -10.2657313871
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -10.265731387085477, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.27)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: -9.98912476865
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -9.989124768648129, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.99)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.55775175648
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 1.84907199346
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.8490719934636757, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.85)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: left, reward: 2.5020631856
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 2.5020631856040954, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.50)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 1.74451690726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.7445169072609095, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.74)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
2.01401065074
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: left, reward: 2.46522463255
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 2.465224632545505, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.47)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: forward, reward: -10.272017432
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -10.272017432018547, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.27)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
1.53269555674
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: 1.16433508688
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.1643350868758973, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.16)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 0.953524466455
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.9535244664551377, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.95)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 1.13732573764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.137325737641977, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.14)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', 'forward')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: 0.953307134421
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 0.9533071344212183, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 0.95)
45% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 129
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (3, 7), deadline = 20
0.612503881157
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6125; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 0.503774601613
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 0.5037746016133009, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.50)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'right')
2.06821462592
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: left, reward: 2.75795385484
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 2.7579538548371736, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.76)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', 'right', None)
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: None, reward: -5.7463749045
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 18, 't': 2, 'action': None, 'reward': -5.746374904495887, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.75)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: None, reward: -4.50361845607
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': -4.503618456069361, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.50)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
2.23961764164
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: left, reward: 2.35778615999
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 2.357786159991057, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.36)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
1.45103698002
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 2.6362894818
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.6362894818028155, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.64)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 0.631440903558
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.6314409035581896, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.63)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'right', 'forward')
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 0.207267608014
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 0.2072676080143374, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent drove right instead of left. (rewarded 0.21)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'left')
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: -9.17066446795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.17066446794513, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.17)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.73112682373
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.7311268237326587, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.73)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
1.87909145588
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.05199716779
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.0519971677911775, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.05)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', None)
2.09375966074
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 1.36237973417
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.3623797341651387, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.36)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.62820659487
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.628206594866301, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.63)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 0.627885501646
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.6278855016458083, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.63)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', None)
1.23191020727
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.2962044102
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.296204410195613, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.30)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 1.09401754488
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.0940175448835547, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.09)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
2.18996816902
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: left, reward: 1.09426077846
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 1.0942607784603173, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.09)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 2.10524770063
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 2.105247700630423, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.11)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 130
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (4, 7), deadline = 25
0.610180783091
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6102; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: -19.7864098002
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': -19.786409800166552, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.79)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: -39.3300874373
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -39.33008743731129, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.33)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: -10.9897631477
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -10.989763147665453, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.99)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: -9.61687375232
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': -9.6168737523212, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.62)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 1.08015553486
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.080155534862747, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 1.08)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: -0.0325690486557
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -0.03256904865574761, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded -0.03)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
1.18640961172
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 1.4068143411
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.4068143410990677, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.41)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: -4.20779957111
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 18, 't': 7, 'action': None, 'reward': -4.207799571113654, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.21)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
1.29661197641
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 2.69802625817
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 2.698026258167888, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.70)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
1.99731911729
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: forward, reward: 2.713549033
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.7135490329992367, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.71)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'left')
2.02612117822
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: left, reward: 0.944884381617
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 0.9448843816172738, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 0.94)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: right, reward: 0.0855820526776
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 0.08558205267755381, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 0.09)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
2.29870190082
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: 1.84179355836
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 1.8417935583587797, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.84)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'left')
1.48550277992
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 1.45531884874
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 1.4553188487377724, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.46)
44% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 131
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (4, 3), deadline = 30
0.607866496045
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6079; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: -5.12311364581
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 30, 't': 0, 'action': None, 'reward': -5.123113645814041, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.12)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'left', 'right')
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: right, reward: 1.52361970127
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 1.5236197012653439, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.52)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: right, reward: 1.85579431401
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 1.8557943140117743, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.86)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'forward')
1.55981502766
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: None, reward: 1.12451880163
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.1245188016335763, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.12)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: None, reward: 2.67113878615
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.6711387861488363, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.67)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 2.29426586039
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 2.2942658603917128, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.29)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: -4.57088061674
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 24, 't': 6, 'action': None, 'reward': -4.570880616740243, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.57)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
1.80551471368
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.22480887775
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.224808877745166, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.22)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
1.51516179571
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 2.88987247252
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.8898724725163776, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.89)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: -5.70469618758
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': -5.704696187575955, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.70)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 1.48001020709
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 1.4800102070866024, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.48)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: left, reward: -19.2274745259
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': -19.22747452593892, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.23)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'forward')
2.23807557001
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: 2.76569173369
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 18, 't': 12, 'action': 'left', 'reward': 2.7656917336864995, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.77)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: forward, reward: 2.06376811565
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 2.063768115652137, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.06)
53% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 132
\-------------------------
Environment.reset(): Trial set up with start = (4, 5), destination = (8, 7), deadline = 30
0.605560986602
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6056; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: right, reward: -19.5241020608
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': -19.52410206081449, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.52)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', 'forward')
0.855191514981
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: None, reward: 2.12801334639
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.1280133463855107, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.13)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', 'right')
1.047186689
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: None, reward: 1.05714135297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.0571413529673075, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.06)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: forward, reward: -10.4975781649
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': -10.497578164945235, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.50)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: forward, reward: -10.7135409634
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': -10.713540963414438, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.71)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'forward', None)
1.87347209132
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: right, reward: 1.04468772777
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 1.0446877277704243, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.04)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'forward')
1.34216691465
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 1.88429778496
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 24, 't': 6, 'action': None, 'reward': 1.8842977849571951, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.88)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
1.96759804744
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 2.73648220232
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 7, 'action': None, 'reward': 2.736482202322377, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.74)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
2.35204012488
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 1.92109294047
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.9210929404724022, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.92)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
2.13656653268
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 2.20830498165
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.2083049816462035, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.21)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', 'forward')
1.52629455218
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 1.50467041139
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.5046704113866367, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.50)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 1.02937480871
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 19, 't': 11, 'action': None, 'reward': 1.0293748087105032, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.03)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: 1.9978722833
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 1.99787228329967, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.00)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: -9.3596778054
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': -9.35967780539762, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.36)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
1.58145877365
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.10296632246
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 2.1029663224596113, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.10)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.14314133145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 15, 't': 15, 'action': None, 'reward': 2.1431413314450682, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.14)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.77100463883
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.771004638825923, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.77)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: left, reward: 0.260025790674
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 17, 'action': 'left', 'reward': 0.26002579067392806, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.26)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
2.13740626536
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: 2.65980744655
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 18, 'action': None, 'reward': 2.6598074465468136, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.66)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: forward, reward: 0.464731072381
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 19, 'action': 'forward', 'reward': 0.46473107238136924, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.46)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'green', None, 'left')
1.98874049827
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 0.784763669453
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 0.7847636694531412, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 0.78)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.18051991421
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 21, 'action': None, 'reward': 2.1805199142109, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.18)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.2023769971
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 22, 'action': None, 'reward': 2.202376997101664, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.20)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, 'right')
1.07157066572
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.28786144213
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 7, 't': 23, 'action': None, 'reward': 1.2878614421306807, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.29)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'left', 'left')
1.33513585905
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 0.767927492567
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 6, 't': 24, 'action': None, 'reward': 0.7679274925673001, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 0.77)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'green', 'left', None)
1.86237689726
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 0.66720511581
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 5, 't': 25, 'action': 'forward', 'reward': 0.6672051158102932, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.67)
13% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 133
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (2, 6), deadline = 20
0.603264221471
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6033; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: 0.414097721071
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 0.4140977210707959, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.41)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 2.17132799044
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.1713279904404477, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.17)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 1.26914423824
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.269144238244909, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 1.27)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: -5.13537040614
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': -5.135370406140652, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.14)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
2.07024772959
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: 2.15303145544
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 2.153031455440679, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.15)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'left')
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: -9.2771803709
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -9.277180370904304, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.28)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'right', None)
1.9183112461
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: 1.75736488543
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.757364885430558, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.76)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'right', 'right')
New state created!
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: -39.2230369554
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', 'right'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': -39.223036955425925, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'right')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.22)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'right', None)
1.83783806577
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: 0.898294318674
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.8982943186742227, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.90)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: right, reward: 1.23537314513
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.23537314512881, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.24)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: left, reward: -0.220039847822
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -0.2200398478221416, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent drove left instead of right. (rewarded -0.22)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: right, reward: -19.5194476347
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': -19.51944763470021, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.52)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
1.66973252028
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: None, reward: 1.76396098786
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.7639609878608153, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.76)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: 1.90770489273
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.9077048927340212, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.91)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: 0.578100341962
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 0.5781003419617319, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 0.58)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.64927728614
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.6492772861437281, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.65)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', 'right')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.10522804226
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 4, 't': 16, 'action': None, 'reward': 2.105228042259456, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.11)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'left', None)
1.72806969745
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.36176493705
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 1.3617649370468985, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.36)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 0.356850445757
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.35685044575682934, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.36)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 0.587515401596
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.5875154015961377, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.59)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 134
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (6, 2), deadline = 20
0.600976167485
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.6010; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: None, reward: 0.355316181787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 0.3553161817868635, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.36)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: right, reward: 1.66702107894
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.6670210789377342, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.67)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
2.11921534749
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: right, reward: 1.52067422709
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.5206742270938205, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.52)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', 'forward')
1.76589444421
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: None, reward: 2.35145490385
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.3514549038484693, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.35)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: None, reward: 1.81382011701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.8138201170133856, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.81)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: right, reward: 1.1144336583
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.1144336583029588, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.11)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: right, reward: -19.4214583766
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': -19.421458376639034, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.42)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: left, reward: 0.156029880103
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 0.1560298801031208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.16)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
1.46554431184
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: right, reward: 2.48293109613
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 2.4829310961340063, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.48)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: left, reward: -9.80231870241
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -9.802318702406644, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.80)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward')
1.09926205497
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: None, reward: 1.25250240682
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.2525024068151553, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.25)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: None, reward: -5.30211224978
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': None, 'reward': -5.302112249775491, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.30)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
2.11163959251
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: 1.67365605671
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 1.6736560567136896, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.67)
35% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 135
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (8, 4), deadline = 20
0.598696791606
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5987; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'left')
1.38675208386
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 1.16445630327
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.164456303269007, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.16)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
2.02394207105
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 1.88294542722
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.8829454272178006, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.88)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'right', None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 1.99706890105
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.997068901052435, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.00)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'right', None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: forward, reward: -10.6617046837
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.661704683688612, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -10.66)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'right', 'right')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: left, reward: -20.4192221418
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'right', 'right'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -20.419222141830552, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'right')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.42)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: -4.95335042668
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': -4.953350426680707, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.95)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
1.97423770399
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 2.24870228387
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 2.24870228387426, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.25)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: left, reward: -40.8052001279
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -40.805200127905536, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.81)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 1.72639755196
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.7263975519583372, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.73)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: -10.9032533408
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -10.903253340826897, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.90)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
1.89264782461
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: 1.42447291837
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 1.4244729183652707, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.42)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
1.77490968324
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: left, reward: 2.39436088576
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 2.3943608857634997, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.39)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
2.09797062546
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 1.51525074033
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.5152507403272817, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.52)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: 1.02246495038
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.0224649503776657, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.02)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.46407788863
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.464077888630451, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.46)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', None)
1.18183920987
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 0.62700346531
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.6270034653101584, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.63)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: -9.88973300656
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -9.889733006562198, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.89)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: -4.26941698367
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 3, 't': 17, 'action': None, 'reward': -4.26941698366669, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.27)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, None)
2.11146999393
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: right, reward: 1.68599852563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.685998525632946, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.69)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: None, reward: -4.16817622784
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': -4.168176227843006, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.17)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 136
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (2, 7), deadline = 30
0.596426060918
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5964; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'left')
1.14772485039
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 2.35698609854
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 2.356986098536656, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.36)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 2.59782660698
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': 2.5978266069751834, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.60)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: right, reward: 0.975794783793
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.9757947837927178, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.98)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: forward, reward: 1.0680025394
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 1.0680025394005543, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.07)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: 2.4731341243
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': 2.4731341242957026, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.47)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
1.57436212923
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 2.12623227073
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 2.1262322707343753, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.13)
80% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 137
\-------------------------
Environment.reset(): Trial set up with start = (5, 6), destination = (1, 6), deadline = 20
0.594163942634
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5942; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: 1.99595936226
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.9959593622602643, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 2.00)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'right', None)
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: -39.9462857141
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -39.94628571411786, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.95)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: forward, reward: -39.0506283242
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -39.05062832419791, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.05)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: forward, reward: 1.06924156757
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.0692415675652343, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent drove forward instead of right. (rewarded 1.07)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
1.95344374913
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: None, reward: 2.88576991091
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.8857699109140245, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.89)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, 'right')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: None, reward: -4.68588294813
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 15, 't': 5, 'action': None, 'reward': -4.685882948129458, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.69)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'right')
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: right, reward: 1.9679800316
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.967980031602225, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.97)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: right, reward: 1.75294157969
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.7529415796935168, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.75)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: left, reward: 2.75865729908
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 2.7586572990780898, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.76)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'right', 'forward')
0.959982993403
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 0.983172351726
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 11, 't': 9, 'action': None, 'reward': 0.9831723517256163, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 0.98)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 1.57920624561
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.579206245613403, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.58)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 1.70327906009
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.7032790600856833, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.70)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'right', 'right')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 1.51808023671
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'right'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.5180802367079305, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'right')
Agent followed the waypoint right. (rewarded 1.52)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
1.89873425978
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 2.26288072989
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 2.2628807298864784, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.26)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: left, reward: 1.47846833299
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 1.4784683329927044, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.48)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', 'left')
1.86230080158
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: None, reward: 2.44645584711
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 5, 't': 15, 'action': None, 'reward': 2.446455847105354, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.45)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: left, reward: -10.8273623163
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -10.82736231630757, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -10.83)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: left, reward: -0.223784792611
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -0.22378479261096829, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded -0.22)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: forward, reward: 0.709523407405
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 0.7095234074047174, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.71)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: left, reward: 0.304257104386
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 0.30425710438567966, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.30)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 138
\-------------------------
Environment.reset(): Trial set up with start = (8, 6), destination = (3, 7), deadline = 20
0.591910404087
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5919; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
2.08080749483
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 2.42998437712
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.4299843771202143, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.43)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: left, reward: -9.99347635248
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -9.993476352481167, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.99)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.8066106829
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 2.45358949318
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.4535894931768434, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.45)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
2.13010008804
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.81259932742
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.8125993274229644, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.81)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
1.15061186109
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.74180748624
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.741807486241802, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.74)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', 'right')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 0.203607945147
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.2036079451465922, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent drove right instead of forward. (rewarded 0.20)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.38467978807
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 0.0245916137892
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.02459161378924013, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.02)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: -40.9623071078
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -40.96230710782901, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.96)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.92345077244
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.9234507724378525, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.92)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.59560779891
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.595607798913748, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.60)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None)
2.14989098156
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.0145840197
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.014584019699673, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.01)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.70744498683
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.7074449868291053, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.71)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'right', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: -5.52020136601
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 8, 't': 12, 'action': None, 'reward': -5.5202013660093305, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.52)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
2.25539593598
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 1.29465325479
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.2946532547879153, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.29)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: left, reward: -10.0485669596
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -10.048566959595057, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.05)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
2.17243575716
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.33985269041
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.3398526904074683, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.34)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: left, reward: 0.388846635724
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 0.3888466357241853, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.39)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, 'right')
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: None, reward: -5.3296701739
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 3, 't': 17, 'action': None, 'reward': -5.3296701739000625, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.33)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: forward, reward: 0.462297247893
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 0.4622972478928362, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.46)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'right', None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: 0.916716038918
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': 0.9167160389176693, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'right', None)
Agent drove forward instead of right. (rewarded 0.92)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 139
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (7, 7), deadline = 20
0.589665412736
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
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epsilon = 0.5897; alpha = 0.5000
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epsilon = 0.5897; alpha = 0.5000
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epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5897; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: 2.8634179139
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.8634179138958915, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.86)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: forward, reward: -10.8975965033
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -10.897596503253176, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.90)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: forward, reward: -10.8697743744
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.869774374379215, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.87)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: forward, reward: -39.2622285161
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -39.262228516115144, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.26)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: -4.25380903954
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': -4.2538090395412205, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.25)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
1.77502459538
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 2.35777962115
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.3577796211536066, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.36)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
1.97134970773
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.64665352647
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.64665352647222, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.65)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 2.0263782354
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.026378235397474, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.03)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'forward')
1.53999199691
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 0.888158529244
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 0.888158529243571, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 0.89)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'left')
2.01146016526
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: 1.89729480109
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.8972948010945727, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.90)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 140
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (1, 5), deadline = 20
0.587428936165
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5874; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', None)
1.45907990954
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 2.02065093132
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.0206509313229954, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.02)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: None, reward: 2.01770294503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.0177029450277253, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.02)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: None, reward: 1.29580907792
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.2958090779228981, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.30)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
1.55118503199
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: 1.39467719159
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.3946771915932032, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.39)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 0.532128597425
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.5321285974253319, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent drove right instead of forward. (rewarded 0.53)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: 1.17506744389
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.1750674438897561, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.18)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: -39.3947419368
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -39.39474193682277, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.39)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', None)
1.50425402945
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.47159455211
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.4715945521081688, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.47)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
1.48792429078
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 0.960014617689
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.9600146176890543, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.96)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
1.22396945423
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 2.41069625491
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.4106962549077835, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.41)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
1.71684675407
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 2.22121654746
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.2212165474640058, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.22)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', 'right')
0.545098972605
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 0.114097077766
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.11409707776572642, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove right instead of left. (rewarded 0.11)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 0.839279908263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.8392799082634133, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.84)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.63937733687
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.6393773368686342, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.64)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: -19.5212946101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': -19.521294610113987, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.52)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -39.0634993745
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -39.0634993745116, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.06)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: -0.588222985985
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': None, 'reward': -0.5882229859845529, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded -0.59)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, 'right')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: -4.49946825597
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 3, 't': 17, 'action': None, 'reward': -4.499468255970946, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.50)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 0.811185644596
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 0.8111856445955452, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.81)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
1.81994478729
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 1.58710047092
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 1.5871004709203202, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.59)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 141
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (4, 6), deadline = 25
0.585200942077
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5852; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
1.30455332391
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.36227583223
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.362275832232103, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.36)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: -9.65154101756
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -9.651541017556681, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.65)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: -9.03595142706
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -9.035951427055176, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.04)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
1.47293111179
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 2.18545392164
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 2.185453921643944, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.19)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'forward')
1.98521644788
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 2.20103715275
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.2010371527472206, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.20)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: -5.84623236057
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 20, 't': 5, 'action': None, 'reward': -5.8462323605684965, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.85)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: -40.3263776698
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': -40.32637766984304, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.33)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: -10.4513987254
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': -10.451398725363905, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.45)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
1.75614422378
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 2.3460776307
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.3460776306986606, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.35)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: left, reward: -10.5721156944
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -10.572115694423253, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.57)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
2.05111092724
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 1.2932345144
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.2932345143991806, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.29)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 0.843406659113
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 0.8434066591126852, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.84)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
2.00856168189
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 1.4625318179
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 1.4625318178976379, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.46)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: 2.68542840497
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 2.685428404968533, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.69)
44% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 142
\-------------------------
Environment.reset(): Trial set up with start = (4, 6), destination = (7, 5), deadline = 20
0.582981398301
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5830; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', None)
1.44620967366
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 1.71813714664
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.7181371466359274, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.72)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: 0.62466083313
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.6246608331297442, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.62)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: forward, reward: -10.2014507162
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.201450716247777, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.20)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', 'forward')
1.88951401611
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: 2.10311928498
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 2.1031192849828466, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.10)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: left, reward: 0.776082327355
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 0.7760823273546663, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.78)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
1.70352262911
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: right, reward: 2.65279995029
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.6527999502864996, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.65)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 1.99954902086
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.9995490208610043, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.00)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: -10.1643156087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': -10.164315608723763, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.16)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: -9.55513697639
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.55513697639332, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.56)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 1.31869221715
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.3186922171516553, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.32)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'left')
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: right, reward: -0.123311408861
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': -0.12331140886099068, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded -0.12)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, 'forward')
1.74714023835
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: right, reward: 1.44128665342
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.441286653421194, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.44)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
2.06640210827
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: right, reward: 2.58019532511
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 2.5801953251053718, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.58)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', None)
1.73986542043
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: right, reward: 1.27590144999
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.2759014499861878, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.28)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'left')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: left, reward: 1.32164529752
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 1.3216452975157778, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.32)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 143
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (8, 5), deadline = 20
0.580770272787
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5808; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
2.1781612897
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 2.03729655684
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.0372965568423007, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.04)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: -5.35642956495
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': -5.356429564945104, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.36)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
1.95437748318
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 1.1336454968
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.1336454967964387, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.13)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None)
2.32329871669
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.95868989217
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.9586898921717875, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.96)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
1.58217341015
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.8789733084
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.878973308395886, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.88)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', 'right')
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: forward, reward: -9.23300857141
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -9.233008571408453, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -9.23)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: forward, reward: 2.28913253336
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.2891325333555015, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.29)
65% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 144
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (1, 6), deadline = 20
0.578567533605
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5786; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', None)
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: right, reward: 0.973520647742
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 0.9735206477422274, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 0.97)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'forward', 'right')
0.513892578747
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: 0.895976967083
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.8959769670829872, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove right instead of left. (rewarded 0.90)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: None, reward: -4.92660281523
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': -4.926602815230337, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.93)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None)
2.14099430443
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: right, reward: 2.43729661734
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.4372966173405857, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.44)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: -10.83432374
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.834323740041935, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.83)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 2.68804016381
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.6880401638110296, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.69)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'right', None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: -40.644565482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -40.6445654819562, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.64)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', None)
1.81733285457
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.98977737971
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.9897773797120184, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.99)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
1.90355511714
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 2.49363658651
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.493636586514622, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.49)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 1.45024128336
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.450241283357537, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove forward instead of left. (rewarded 1.45)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
1.70341187497
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.60515634629
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.6051563462916267, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.61)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'right')
2.41308424038
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: 2.12002033898
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 2.1200203389788808, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.12)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: -10.4500051116
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -10.450005111562065, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.45)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 0.134313481162
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.13431348116221786, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.13)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'forward', None)
2.19859585183
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.12084260194
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.1208426019351156, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.12)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: -10.3060726991
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -10.306072699080872, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.31)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
1.96903165077
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.0037664168
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.0037664167980005, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.00)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
2.38503184473
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 0.953362659291
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 0.9533626592911826, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.95)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: 0.511070058328
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 0.5110700583279382, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.51)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: left, reward: -19.6097865734
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -19.609786573403486, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.61)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 145
\-------------------------
Environment.reset(): Trial set up with start = (5, 3), destination = (1, 6), deadline = 35
0.576373148949
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5764; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: -4.09428046523
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 35, 't': 0, 'action': None, 'reward': -4.094280465228161, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.09)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: -5.03732344351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 34, 't': 1, 'action': None, 'reward': -5.03732344350716, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.04)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', 'right', None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 1.6958131509
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 33, 't': 2, 'action': 'right', 'reward': 1.695813150895439, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 1.70)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'right', None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: -5.15531605883
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 32, 't': 3, 'action': None, 'reward': -5.155316058831186, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.16)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'left', None)
1.54491731725
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: right, reward: 1.61105895353
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 31, 't': 4, 'action': 'right', 'reward': 1.6110589535318436, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.61)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
2.20251713411
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: None, reward: 2.74785183288
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 30, 't': 5, 'action': None, 'reward': 2.7478518328775796, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.75)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: -10.4248959144
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 29, 't': 6, 'action': 'left', 'reward': -10.424895914428479, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.42)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: right, reward: 0.737874269557
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 28, 't': 7, 'action': 'right', 'reward': 0.7378742695568682, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.74)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'right')
2.26655228968
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: 1.84801828687
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 27, 't': 8, 'action': 'left', 'reward': 1.8480182868685853, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.85)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 1.89220466631
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 9, 'action': 'right', 'reward': 1.8922046663055125, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.89)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
1.35825468894
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 1.49870715567
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 10, 'action': 'left', 'reward': 1.4987071556708427, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.50)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: -10.679632643
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 24, 't': 11, 'action': 'left', 'reward': -10.679632643011914, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.68)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: right, reward: 0.301490403008
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 12, 'action': 'right', 'reward': 0.301490403008145, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.30)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: -19.3316428617
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 22, 't': 13, 'action': 'left', 'reward': -19.33164286168777, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.33)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: left, reward: 1.06072864247
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 14, 'action': 'left', 'reward': 1.0607286424746476, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.06)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
2.10772892327
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: right, reward: 2.66425603586
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 15, 'action': 'right', 'reward': 2.664256035863698, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.66)
54% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 146
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (6, 2), deadline = 20
0.574187087131
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
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epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5742; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 1.63178045127
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.6317804512713363, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.63)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: -20.7011603926
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': -20.7011603925602, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.70)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
1.82919251672
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 1.70455595056
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.7045559505565417, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.70)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: 1.74604872225
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.7460487222529513, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.75)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: left, reward: -9.28482296677
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -9.284822966765624, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.28)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: right, reward: 1.54150347907
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.5415034790655406, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.54)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: left, reward: 0.895641293586
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 0.8956412935857232, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.90)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', 'forward')
2.05777046613
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: forward, reward: 1.51837622568
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.5183762256802285, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 1.52)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: 1.6581354647
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.658135464696819, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.66)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: -19.510826834
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -19.510826833959992, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.51)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: -40.4647224595
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -40.46472245946159, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.46)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 0.996829497951
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.9968294979506107, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.00)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: -9.86496791012
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -9.86496791011655, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.86)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
1.20169048363
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 0.644653515665
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.6446535156654752, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.64)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: -10.0276050456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -10.027605045560946, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.03)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: right, reward: -0.369803605366
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': -0.36980360536622503, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded -0.37)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: left, reward: -20.3395597693
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -20.33955976930917, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.34)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
2.28914546088
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: right, reward: 0.62580859287
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.6258085928699513, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.63)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: -4.32032322739
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 2, 't': 18, 'action': None, 'reward': -4.320323227388951, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.32)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
1.90427531898
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 1.55030966492
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.550309664915906, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.55)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 147
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (5, 3), deadline = 25
0.572009316584
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5720; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 2.47896883944
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 2.4789688394394727, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.48)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 2.21478831633
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 2.2147883163293125, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.21)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
1.97516753311
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 2.07592371318
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 2.0759237131763286, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.08)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 1.05998078414
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.0599807841443114, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent drove right instead of forward. (rewarded 1.06)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'left')
1.39602805592
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: left, reward: 1.30060155622
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.3006015562155258, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.30)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
1.07012303799
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: left, reward: 1.3693796675
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 1.369379667502181, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.37)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: forward, reward: -10.4651713321
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': -10.465171332090508, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.47)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
1.18639552499
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: forward, reward: 1.08853554055
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.0885355405549437, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.09)
68% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 148
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (3, 5), deadline = 20
0.569839805862
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5698; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: 1.30527738157
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.305277381571352, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.31)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
1.45747702688
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 2.00444791009
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.0044479100900126, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.00)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: -10.5121036773
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.512103677306301, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.51)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 1.83410734281
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.834107342805619, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.83)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: right, reward: 1.12671651204
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.1267165120367277, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 1.13)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'right', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: -20.0203637957
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': -20.0203637957179, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.02)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.21975135274
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: left, reward: 1.88420803896
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 1.8842080389578395, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.88)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', 'forward')
1.7880733459
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: 2.30187268339
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.3018726833946106, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.30)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: forward, reward: 1.35029331061
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.3502933106116135, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.35)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'forward', None)
1.50788343521
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: right, reward: 1.25747578839
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.2574757883873102, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.26)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, 'left')
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: forward, reward: 0.272478096132
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 0.2724780961317067, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded 0.27)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'left')
1.83341457807
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: None, reward: 0.821083235597
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.8210832355966629, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.82)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: left, reward: -9.84868956779
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -9.848689567786634, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.85)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: right, reward: 2.43856702181
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 2.4385670218078133, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.44)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'right', 'forward')
1.01048051494
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: 0.644931604391
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.6449316043914675, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent followed the waypoint right. (rewarded 0.64)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 149
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (6, 4), deadline = 25
0.567678523637
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
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epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5677; alpha = 0.5000
Simulating trial. . .
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/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: left, reward: -20.0658854234
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -20.065885423446655, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.07)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: right, reward: 1.07301091609
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.0730109160938566, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.07)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: -5.67350823928
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': -5.673508239280297, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.67)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None)
2.08476474515
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: right, reward: 2.57138139191
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 2.5713813919055166, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.57)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 2.1930031799
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.193003179900325, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.19)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, 'left')
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 2.46795224437
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 2.467952244365561, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.47)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 1.9277253412
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.9277253411989042, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.93)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 0.805548326316
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 0.8055483263164553, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.81)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: forward, reward: -40.3440567522
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -40.34405675218889, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.34)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None)
1.48639903378
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.23427087073
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.234270870731426, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.23)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: left, reward: -9.29064132831
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -9.290641328312207, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.29)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
1.66919725201
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: 0.95216623386
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 0.9521662338598242, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.95)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -9.66205545507
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': -9.66205545507308, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -9.66)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
1.72729249195
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 0.906776079202
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': 0.9067760792024975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.91)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: -0.262996603896
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': -0.2629966038960768, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded -0.26)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', None)
1.31068174294
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: left, reward: 1.92673915013
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 1.9267391501269218, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.93)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'right', None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: left, reward: -40.7482202412
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -40.74822024122717, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.75)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: 1.2790913707
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 1.279091370702688, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.28)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'left')
1.54401148999
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: forward, reward: 2.49481793853
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': 2.4948179385318285, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.49)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: forward, reward: -10.9554076767
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': -10.955407676686232, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.96)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'right', None)
1.34622813292
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: None, reward: 1.1265449649
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.1265449648992003, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.13)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: None, reward: 0.38014153922
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 4, 't': 21, 'action': None, 'reward': 0.3801415392201406, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.38)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: 0.544897666472
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 0.5448976664715188, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.54)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', 'left', None)
2.08223750063
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 0.637051925592
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.6370519255916749, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.64)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', 'left', 'right')
1.75099746782
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 0.411655966406
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.41165596640567714, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 0.41)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 150
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (8, 6), deadline = 20
0.5655254387
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5655; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'forward')
2.30032293993
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 2.24336060099
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.243360600990395, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.24)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'right', 'right')
1.10677422237
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 1.94271772663
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'right'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.9427177266298072, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 1.94)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: -40.9097226072
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -40.90972260717747, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.91)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'forward', None)
1.3826796118
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 2.16942566991
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.1694256699072128, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.17)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.97663548217
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 1.82117604115
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.8211760411506255, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.82)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: left, reward: 0.557114412278
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 0.5571144122783338, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded 0.56)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
1.13746553277
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: forward, reward: 0.924652588378
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 0.9246525883784087, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.92)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.31703428558
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.29661439326
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.296614393259087, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.30)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.30682433942
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.01825265369
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.0182526536904146, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.02)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.25715892617
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 0.717899059411
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.7178990594113368, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.72)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 1.63075978457
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.6307597845676312, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.63)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: -40.0116914952
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -40.011691495205696, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.01)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 2.03228404166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 2.032284041663712, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.03)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.86937882916
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.8693788291637345, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.87)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
1.89890576166
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: 0.913840281923
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.9138402819227309, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.91)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 0.723254837323
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.7232548373230283, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.72)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: -10.5135258932
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -10.513525893228575, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.51)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'right', None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: -0.28074186597
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': -0.2807418659697679, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded -0.28)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: forward, reward: -39.4820782193
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': -39.482078219327335, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.48)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 1.17189494156
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 1.1718949415555011, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'forward', 'left')
Agent followed the waypoint right. (rewarded 1.17)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 151
\-------------------------
Environment.reset(): Trial set up with start = (8, 3), destination = (2, 6), deadline = 25
0.563380519959
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5634; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'left')
1.03917496973
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 1.08994580718
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.0899458071753563, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.09)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: -10.2560137703
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -10.256013770328966, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.26)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
2.15971922688
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 2.86477074636
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.8647707463607004, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.86)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: -9.8384848813
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -9.838484881303659, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.84)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 1.27723937607
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.2772393760747645, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.28)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 2.76355696831
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 2.763556968306286, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.76)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 2.41918717606
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 2.4191871760611177, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 2.42)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', 'left')
1.05153167581
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.38579959758
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.385799597579681, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.39)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: left, reward: -9.84798941453
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -9.8479894145294, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -9.85)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 0.142434775132
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.14243477513170633, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.14)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: -10.5124429188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -10.512442918822941, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.51)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'left', None)
1.36033495226
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 0.974056294997
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 14, 't': 11, 'action': None, 'reward': 0.9740562949972345, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.97)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: -9.65345021209
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': -9.653450212085703, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.65)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', 'right')
0.520597690271
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: forward, reward: 0.421212822161
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 0.42121282216129163, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 0.42)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
1.41553553328
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: 2.27565810401
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': 2.2756581040066894, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.28)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: -5.40682163493
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 10, 't': 15, 'action': None, 'reward': -5.406821634927153, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.41)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: -0.128002227123
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -0.12800222712283282, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded -0.13)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', None)
1.35964471311
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 1.12768008552
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 8, 't': 17, 'action': None, 'reward': 1.1276800855168236, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.13)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None)
1.30012444589
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 2.31606758959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 2.316067589585855, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.32)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: -0.376583239143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': -0.37658323914293945, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded -0.38)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, 'left')
1.06456038845
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: None, reward: 1.55278537153
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.5527853715294548, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.55)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: right, reward: -20.9883373587
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 4, 't': 21, 'action': 'right', 'reward': -20.98833735871192, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.99)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: 0.262717738989
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 0.2627177389885724, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.26)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: left, reward: -0.140670358361
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -0.14067035836064767, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.14)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: -5.00468743354
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 1, 't': 24, 'action': None, 'reward': -5.004687433538868, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.00)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 152
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (3, 2), deadline = 25
0.561243736443
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5612; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'left')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: right, reward: 0.115673969471
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.11567396947112663, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove right instead of left. (rewarded 0.12)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: 1.25436560756
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.2543656075567489, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.25)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
1.75646147794
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: right, reward: 1.90807419919
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.9080741991940386, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.91)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
1.40637302179
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 2.93411283068
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 2.9341128306792488, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.93)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.03105906057
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 2.73065458413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.730654584127233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.73)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 0.0630362369539
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.06303623695385563, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.06)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'right', 'right')
0.0
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: left, reward: -20.2740029876
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'right'}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'right', 'right'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -20.27400298759023, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'right')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.27)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.84559681864
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: left, reward: 1.0818904527
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 1.081890452702706, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.08)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 1.88318395513
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.8831839551291547, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.88)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.46374363567
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: left, reward: 1.73937116048
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 1.7393711604775466, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.74)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'left')
2.4751844835
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: None, reward: 1.90770298106
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.9077029810635155, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.91)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: None, reward: 1.86634298796
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.8663429879569975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.87)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: right, reward: 1.7135513123
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.7135513123027, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.71)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'forward')
1.17588223089
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 2.31529233263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.315292332634827, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.32)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: left, reward: -10.1126323825
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -10.112632382453924, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.11)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
1.16719562363
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 2.36190602199
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 2.3619060219927093, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.36)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
1.76455082281
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 1.48621687206
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.4862168720622593, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.49)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: forward, reward: 0.963646849367
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 0.9636468493666994, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove forward instead of left. (rewarded 0.96)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: right, reward: -20.1508050192
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': -20.15080501922198, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.15)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', None)
1.62538384744
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 1.2943881026
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.294388102600008, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.29)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: forward, reward: -9.34416581008
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': -9.344165810082156, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.34)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: left, reward: 2.03640429615
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 2.0364042961472193, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.04)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, 'left')
1.34831480607
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: left, reward: 1.03046523406
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 1.0304652340559033, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.03)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, None)
1.51444074226
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: None, reward: 0.420581397421
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.42058139742094247, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.42)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'right', 'forward')
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: left, reward: -39.3248877622
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 1, 't': 24, 'action': 'left', 'reward': -39.32488776223328, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.32)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 153
\-------------------------
Environment.reset(): Trial set up with start = (5, 6), destination = (1, 4), deadline = 30
0.559115057297
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5591; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', None)
2.37887751524
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: 2.74817627734
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': 2.748176277335348, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.75)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: -40.7586089245
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': -40.75860892451907, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.76)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: -9.34545012913
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': -9.345450129131006, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.35)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: -9.21750029542
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': -9.217500295423502, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.22)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'right')
1.17971605393
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 2.60320131133
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.60320131133446, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.60)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
2.17024292624
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 2.8639202026
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 2.8639202025962422, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.86)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: left, reward: -20.2408841116
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 24, 't': 6, 'action': 'left', 'reward': -20.240884111564153, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.24)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 1.13065412761
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.1306541276142812, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.13)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: left, reward: -9.8317941347
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': 'left', 'reward': -9.83179413470488, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.83)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 2.09453479526
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.0945347952613123, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.09)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
1.88085682235
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: 2.03636675591
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 2.0363667559094285, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.04)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
2.56352689629
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: 1.16942817259
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.1694281725909423, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.17)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 0.131647426418
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 0.13164742641813032, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.13)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: -0.14271020661
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': -0.14271020660995815, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded -0.14)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, 'left')
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: None, reward: 1.7965097573
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.7965097573033246, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.80)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
2.40721877909
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 1.64186954491
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 15, 'action': 'right', 'reward': 1.6418695449148097, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.64)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -9.23941792306
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 16, 'action': 'left', 'reward': -9.239417923059767, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.24)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'right', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: -4.99621858777
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 13, 't': 17, 'action': None, 'reward': -4.996218587773354, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.00)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: left, reward: 0.804395327247
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 12, 't': 18, 'action': 'left', 'reward': 0.8043953272473638, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 0.80)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, 'left')
2.01941471426
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: 1.36267283699
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 11, 't': 19, 'action': 'forward', 'reward': 1.3626728369901955, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.36)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', None, 'left')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: 0.809620627594
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 10, 't': 20, 'action': 'forward', 'reward': 0.8096206275942943, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 0.81)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 154
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (4, 4), deadline = 25
0.556994451781
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5570; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: None, reward: 1.48402762893
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.4840276289290855, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.48)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: None, reward: -4.74493420582
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': -4.744934205823779, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.74)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: 1.8364760614
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 1.8364760614031264, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.84)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: left, reward: 0.675245300226
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 22, 't': 3, 'action': 'left', 'reward': 0.6752453002255894, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded 0.68)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 0.227701702841
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 0.22770170284058933, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.23)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: -5.96305606745
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': -5.96305606744988, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.96)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 2.48377854281
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 2.4837785428089862, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.48)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: -5.41952576109
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 18, 't': 7, 'action': None, 'reward': -5.419525761087492, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.42)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'forward', 'left')
1.66459116077
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 1.38864379484
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.3886437948397483, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.39)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 1.64843942725
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.6484394272460379, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.65)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: -9.08588043946
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': -9.085880439455945, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.09)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: left, reward: -19.2876552081
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -19.287655208141825, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.29)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: 0.330628463996
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 0.33062846399595647, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.33)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: right, reward: 2.39557801401
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 2.3955780140069303, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.40)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: -0.16768992314
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': -0.16768992314014397, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded -0.17)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
1.76873403288
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 1.66642822296
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.6664282229619294, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.67)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: -4.8573981226
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 9, 't': 16, 'action': None, 'reward': -4.857398122598751, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.86)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, 'forward')
1.00923529516
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 0.6785037697
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 0.6785037696999765, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 0.68)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'forward', None)
1.77605264085
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 0.633629372728
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 0.6336293727278157, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 0.63)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: -0.201377366758
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': -0.20137736675753437, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.20)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 1.38419376773
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': 1.3841937677288296, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.38)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', None)
0.240037636769
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: 0.154720757936
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.15472075793568074, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.15)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: right, reward: -0.592749827626
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': -0.5927498276255989, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded -0.59)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', 'right', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: -40.8329867261
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -40.832986726101325, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.83)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, None)
2.024544162
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 0.210395854721
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.21039585472120326, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.21)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 155
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (6, 2), deadline = 30
0.554881889275
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
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epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5549; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, None)
2.51708156442
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 1.31522053359
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': 1.3152205335926757, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.32)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.62811220991
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.6281122099129517, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 1.63)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'left', 'left')
1.52833967715
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 2.92404715636
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': 2.9240471563622394, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.92)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 1.76520697764
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 1.7652069776420176, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.77)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 0.810717236867
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 0.8107172368667676, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.81)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.71758112792
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: 1.78899430098
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.788994300982627, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.79)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: 2.0614895159
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.0614895159037254, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.06)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.60155739807
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: 0.9924667734
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 0.9924667733999999, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.99)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.07266369108
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.0726636910839313, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'right', None)
0.107476415339
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 0.193529474303
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 0.19352947430344591, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.19)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
1.45988597502
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 2.44804215642
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.448042156417456, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.45)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 1.77035370185
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 1.7703537018494089, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.77)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, 'right')
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: -5.46937531833
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 18, 't': 12, 'action': None, 'reward': -5.469375318333506, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.47)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 1.01202216145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 13, 'action': None, 'reward': 1.0120221614462308, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.01)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
1.45030496754
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 1.69369741324
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.6936974132442861, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.69)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: -39.7676817118
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -39.7676817117745, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.77)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, 'right')
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 2.10635070858
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 2.1063507085775814, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent followed the waypoint right. (rewarded 2.11)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.33926761326
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 17, 'action': None, 'reward': 1.3392676132581423, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.34)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', None)
1.67217272082
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.92995648879
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 18, 'action': None, 'reward': 1.9299564887869138, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.93)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: forward, reward: 1.62853685485
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 19, 'action': 'forward', 'reward': 1.6285368548461359, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.63)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: right, reward: 1.91435212474
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 1.9143521247411346, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.91)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, 'left')
1.25033220161
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: forward, reward: 0.915825288683
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 0.9158252886832499, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 0.92)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 2.02870470889
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 22, 'action': 'forward', 'reward': 2.0287047088890056, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.03)
23% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 156
\-------------------------
Environment.reset(): Trial set up with start = (3, 6), destination = (6, 3), deadline = 30
0.552777339274
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5528; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', 'forward')
2.04497301465
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: forward, reward: 2.58753634354
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': 2.5875363435415553, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.59)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 0.0419917092556
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 29, 't': 1, 'action': None, 'reward': 0.04199170925555851, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded 0.04)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: 1.85799196693
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 1.857991966931114, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent drove right instead of forward. (rewarded 1.86)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', 'forward')
1.4768299377
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 1.05314477259
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.0531447725911738, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.05)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
1.95396406572
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 1.65700504888
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.657005048878332, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.66)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: 2.39285668017
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'left', 'reward': 2.3928566801742157, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.39)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'left')
1.08307874515
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 2.59789941058
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 2.5978994105768676, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.60)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: -9.60985966531
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': -9.609859665307857, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.61)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'left', None)
1.80809601774
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 1.62559239639
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.625592396389393, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.63)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', 'forward')
1.11022480356
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: forward, reward: 2.41239006217
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 2.4123900621716063, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 2.41)
67% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 157
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (5, 4), deadline = 30
0.550680771386
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5507; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: left, reward: 1.13906974827
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 1.1390697482744954, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.14)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: left, reward: -9.62011820924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 29, 't': 1, 'action': 'left', 'reward': -9.620118209244929, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.62)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.3236214309
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.67535567258
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.6753556725838465, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.68)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.38327487909
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.383274879093196, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.38)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'left')
2.19144373228
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.00565785189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.005657851891862, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.01)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 0.171840496517
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 0.17184049651681998, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.17)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: -40.4427347336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': -40.44273473356111, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.44)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.29701208574
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: 2.48380233193
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 2.48380233192639, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.48)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.54380830067
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.543808300669581, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.54)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 1.83298724655
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 1.8329872465454378, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent drove right instead of forward. (rewarded 1.83)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', 'right')
0.330235960567
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: 0.546505294671
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 0.5465052946707947, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent drove right instead of left. (rewarded 0.55)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'right', 'left')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: -10.9208167233
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'right', 'left'), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': -10.92081672329531, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'left')
Agent attempted driving forward through a red light. (rewarded -10.92)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'right', None)
1.36806619222
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.94070491759
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.9407049175917486, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.94)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: -9.01012773006
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 13, 'action': 'left', 'reward': -9.010127730056652, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.01)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: -9.37048901352
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 14, 'action': 'left', 'reward': -9.370489013523885, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.37)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: -10.7443586924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -10.744358692390778, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.74)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, 'forward')
0.877770796975
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: -0.134013041573
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 14, 't': 16, 'action': 'right', 'reward': -0.13401304157303962, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded -0.13)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', None)
1.71684420706
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: right, reward: 2.46189500961
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 2.4618950096122845, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.46)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'forward')
1.92916688898
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 1.16635404143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': 1.1663540414338809, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.17)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, 'left')
2.09855079209
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: None, reward: 0.929588390297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 11, 't': 19, 'action': None, 'reward': 0.9295883902965154, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.93)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: left, reward: -40.6639489522
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 10, 't': 20, 'action': 'left', 'reward': -40.66394895223313, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 1.34176084122
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 1.3417608412237412, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.34)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
1.89040720883
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: left, reward: 2.25912422657
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 22, 'action': 'left', 'reward': 2.2591242265723777, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.26)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', None, None)
1.65709436009
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: forward, reward: 1.17781137235
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 23, 'action': 'forward', 'reward': 1.1778113723470394, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.18)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: right, reward: 1.39978487751
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 6, 't': 24, 'action': 'right', 'reward': 1.3997848775052681, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent drove right instead of forward. (rewarded 1.40)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: 0.990027154911
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 5, 't': 25, 'action': None, 'reward': 0.990027154911411, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.99)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: left, reward: 1.1415764661
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 4, 't': 26, 'action': 'left', 'reward': 1.1415764661039645, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.14)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'green', None, 'left')
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: forward, reward: 0.656045979211
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 3, 't': 27, 'action': 'forward', 'reward': 0.6560459792109059, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 0.66)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'red', 'left', None)
1.8054845573
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: None, reward: 0.715573830723
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 2, 't': 28, 'action': None, 'reward': 0.7155738307229138, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.72)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('left', 'green', 'left', 'right')
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: right, reward: -0.108205619873
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 1, 't': 29, 'action': 'right', 'reward': -0.10820561987281985, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove right instead of left. (rewarded -0.11)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 158
\-------------------------
Environment.reset(): Trial set up with start = (2, 6), destination = (4, 3), deadline = 25
0.548592155339
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5486; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: left, reward: 2.69214049171
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 2.6921404917099676, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.69)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', 'right')
0.438370627619
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 1.4351546056
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.4351546056013829, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent drove right instead of left. (rewarded 1.44)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -9.1101368732
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -9.110136873198522, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.11)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: -10.3726201094
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -10.372620109355319, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.37)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
1.26052919401
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.04924203053
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.0492420305285846, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.05)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', 'forward')
2.13492780126
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: left, reward: 0.990416336717
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 0.9904163367165755, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 0.99)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
2.00578356335
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: 2.16615177589
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 2.166151775893658, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.17)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.41745286622
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: 2.23420326177
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 2.234203261769716, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.23)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
2.36759500804
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 0.984357405726
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 0.9843574057257116, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.98)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.8010646048
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 2.70383000313
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.703830003128972, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.70)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', 'right')
1.16827574944
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 1.68045832622
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.6804583262178543, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 1.68)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
2.32807306853
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: right, reward: 2.13755366256
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 2.1375536625614453, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.14)
52% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 159
\-------------------------
Environment.reset(): Trial set up with start = (3, 7), destination = (5, 5), deadline = 20
0.546511460971
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5465; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'forward')
1.89816171689
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: right, reward: 1.97286239931
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.972862399314212, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.97)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'forward')
1.59421344589
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: right, reward: 2.21869800623
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.21869800622638, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.22)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.67597620688
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 2.33246950567
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.332469505666837, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.33)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: -0.0268958838951
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -0.026895883895092676, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded -0.03)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: -4.08633393165
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 16, 't': 4, 'action': None, 'reward': -4.086333931645274, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.09)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 0.931171915808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 0.9311719158081262, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.93)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: -10.7535957175
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -10.753595717532534, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.75)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: -19.068205001
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': -19.06820500096197, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.07)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
1.83226783857
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 1.03801449694
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.0380144969400276, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.04)
55% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 160
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (2, 2), deadline = 20
0.544438658239
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5444; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: right, reward: 1.6352512299
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.6352512299044004, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent followed the waypoint right. (rewarded 1.64)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: 0.839985482099
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 0.8399854820989051, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.84)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', 'left', 'right')
1.42896836695
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 1.15249423348
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.1524942334752784, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.15)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: -4.93866386914
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': -4.938663869142447, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.94)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'right')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 0.23503130807
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 0.23503130807040373, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove forward instead of left. (rewarded 0.24)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'left')
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: 1.79857678239
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.7985767823930243, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 1.80)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: right, reward: 1.31596077013
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.3159607701269145, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.32)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: None, reward: -5.36722319137
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': None, 'reward': -5.367223191369427, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.37)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: forward, reward: 1.46397638477
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.4639763847699432, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent drove forward instead of right. (rewarded 1.46)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: 0.254490942277
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 0.25449094227672386, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded 0.25)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'right', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.64472932579
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.644729325791481, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.64)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 2.26740500395
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.267405003947161, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.27)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 1.27555613013
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.2755561301289151, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 1.28)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: -9.75141760779
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -9.751417607791277, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent attempted driving forward through a red light. (rewarded -9.75)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: -9.27998684661
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -9.279986846609848, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.28)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: -10.6787317342
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -10.678731734154232, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.68)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: -9.99888136412
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -9.99888136411953, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.00)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 0.961742266531
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': None, 'reward': 0.9617422665308119, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.96)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 0.876771188148
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.8767711881478553, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.88)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, 'left')
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: -5.89069145517
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 1, 't': 19, 'action': None, 'reward': -5.89069145517147, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.89)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 161
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (1, 7), deadline = 20
0.542373717211
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5424; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'right', 'left')
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 1.35156918306
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.3515691830646166, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent drove forward instead of right. (rewarded 1.35)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
2.25010918475
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: right, reward: 1.6911349851
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.6911349851047703, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.69)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 2.44349277341
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.4434927734131917, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.44)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: forward, reward: -10.5999801347
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.599980134717653, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.60)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: left, reward: -10.8616787259
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -10.861678725863982, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.86)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: forward, reward: 1.90929899504
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.909298995039848, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 1.91)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: None, reward: 1.07477061767
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.074770617672692, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.07)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: None, reward: -5.88422057189
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 13, 't': 7, 'action': None, 'reward': -5.884220571893804, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.88)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: forward, reward: 1.78216624279
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.7821662427924045, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 1.78)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, 'left')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: right, reward: 2.27771993123
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 2.2777199312282317, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.28)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: -5.56801387971
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 10, 't': 10, 'action': None, 'reward': -5.568013879707664, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.57)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.82582806399
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: 2.57926706418
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 2.5792670641779383, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.58)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: -0.0355634582621
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': -0.03556345826209317, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded -0.04)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, 'right')
1.84423931343
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: 1.90068881478
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.9006888147848837, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 1.90)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 1.8549306726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.8549306725980999, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.85)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 162
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (2, 3), deadline = 35
0.540316608068
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
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epsilon = 0.5403; alpha = 0.5000
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epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5403; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.99002615313
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.50479788109
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 35, 't': 0, 'action': None, 'reward': 1.5047978810889624, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.50)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, 'right')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 2.49864500469
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 34, 't': 1, 'action': None, 'reward': 2.4986450046900464, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.50)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: -9.07235981262
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 33, 't': 2, 'action': 'forward', 'reward': -9.072359812618597, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.07)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.29493528684
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 32, 't': 3, 'action': None, 'reward': 1.294935286836015, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.29)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'forward')
1.74558728176
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.04641663126
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 31, 't': 4, 'action': None, 'reward': 1.0464166312564247, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.05)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 0.0624739475383
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 30, 't': 5, 'action': 'right', 'reward': 0.062473947538259855, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.06)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 1.34296954769
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 29, 't': 6, 'action': None, 'reward': 1.342969547686887, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.34)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'left', 'right')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 2.37507226484
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 28, 't': 7, 'action': 'right', 'reward': 2.37507226484465, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.38)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
2.20254756409
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 2.2106846963
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 27, 't': 8, 'action': 'forward', 'reward': 2.2106846962997984, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.21)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: 1.21684818142
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 26, 't': 9, 'action': 'forward', 'reward': 1.216848181419289, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.22)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
2.03234035104
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: 2.37482870574
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 10, 'action': None, 'reward': 2.374828705738843, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.37)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: right, reward: 0.633255245115
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 24, 't': 11, 'action': 'right', 'reward': 0.6332552451151366, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.63)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
1.52117365197
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.82232022586
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 12, 'action': None, 'reward': 2.8223202258583093, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.82)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.84023730544
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 13, 'action': None, 'reward': 2.840237305442197, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.84)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', None)
1.65438555491
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.38572723021
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 21, 't': 14, 'action': None, 'reward': 2.3857272302115637, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.39)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', None)
2.51224498662
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 1.57173040486
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 15, 'action': None, 'reward': 1.5717304048619951, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.57)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
2.08596766962
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: left, reward: 0.825335031461
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 19, 't': 16, 'action': 'left', 'reward': 0.8253350314607371, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.83)
51% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: -39.9277393709
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 17, 'action': 'forward', 'reward': -39.92773937085127, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.93)
49% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: -10.2662049043
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 18, 'action': 'forward', 'reward': -10.266204904310426, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.27)
46% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'left', 'forward')
1.9355120581
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: 1.35987264858
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 16, 't': 19, 'action': 'right', 'reward': 1.359872648584511, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.36)
43% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', None, None)
2.20661613019
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: 1.12935548773
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 20, 'action': 'forward', 'reward': 1.1293554877337046, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.13)
40% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 0.358795804823
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 21, 'action': 'right', 'reward': 0.35879580482313567, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.36)
37% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: -4.86101295035
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 13, 't': 22, 'action': None, 'reward': -4.86101295034587, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.86)
34% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'right', 'forward')
0.296334096767
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: right, reward: -0.0132060604929
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 12, 't': 23, 'action': 'right', 'reward': -0.013206060492911842, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent drove right instead of left. (rewarded -0.01)
31% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, 'left')
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: -4.58105096797
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 11, 't': 24, 'action': None, 'reward': -4.581050967971909, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.58)
29% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'red', None, 'left')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 2.36194704122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 10, 't': 25, 'action': 'right', 'reward': 2.361947041218037, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.36)
26% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'red', None, 'forward')
1.60150684313
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 1.50634102957
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 9, 't': 26, 'action': None, 'reward': 1.5063410295658626, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.51)
23% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: right, reward: 1.24685147006
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 27, 'action': 'right', 'reward': 1.2468514700624114, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.25)
20% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('forward', 'red', 'forward', None)
2.20358452839
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: None, reward: 0.501371268081
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 7, 't': 28, 'action': None, 'reward': 0.5013712680812685, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.50)
17% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: None, reward: -5.72017844755
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 6, 't': 29, 'action': None, 'reward': -5.7201784475473865, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.72)
14% of time remaining to reach destination.
/-------------------
| Step 30 Results
\-------------------
Environment.step(): t = 30
('forward', 'green', None, 'forward')
1.4737815888
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: left, reward: 1.23417450675
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 5, 't': 30, 'action': 'left', 'reward': 1.2341745067478151, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.23)
11% of time remaining to reach destination.
/-------------------
| Step 31 Results
\-------------------
Environment.step(): t = 31
('right', 'green', 'right', 'right')
0.543120956601
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: -0.429085995237
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'right'), 'deadline': 4, 't': 31, 'action': 'forward', 'reward': -0.4290859952365297, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'right')
Agent drove forward instead of right. (rewarded -0.43)
9% of time remaining to reach destination.
/-------------------
| Step 32 Results
\-------------------
Environment.step(): t = 32
('right', 'green', None, None)
1.91277637876
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: right, reward: 0.419484973969
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 32, 'action': 'right', 'reward': 0.4194849739692148, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.42)
6% of time remaining to reach destination.
/-------------------
| Step 33 Results
\-------------------
Environment.step(): t = 33
('right', 'red', None, 'left')
2.2093317125
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: right, reward: 0.206645132395
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 2, 't': 33, 'action': 'right', 'reward': 0.20664513239536064, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 0.21)
3% of time remaining to reach destination.
/-------------------
| Step 34 Results
\-------------------
Environment.step(): t = 34
('forward', 'green', None, None)
1.66798580896
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: 0.178318543972
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 34, 'action': 'forward', 'reward': 0.1783185439722339, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.18)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 163
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (2, 3), deadline = 20
0.538267301107
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5383; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: left, reward: 1.03111334014
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.0311133401431545, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent drove left instead of right. (rewarded 1.03)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: 1.71209748035
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 1.712097480346681, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 1.71)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'forward')
1.90645572606
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 1.71617302889
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.71617302888929, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.72)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: -10.8939473327
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.893947332671178, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.89)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
0.923152176468
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 1.14475315992
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.144753159924375, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.14)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: right, reward: -20.1286114737
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': -20.128611473741163, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.13)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 1.28373387624
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.2837338762397097, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.28)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', 'forward')
1.88078380895
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 1.30734745464
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.3073474546390569, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.31)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'right')
1.73862528123
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 2.69892420188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.698924201880554, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.70)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
1.92486601041
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 2.37393413024
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 2.373934130241628, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.37)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
2.00422285628
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 1.83837232122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.8383723212151466, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.84)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
1.92129758875
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 1.87330651751
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.8733065175118222, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.87)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.40235477765
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.4023547776476004, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.40)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 1.01154569973
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.0115456997347392, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.01)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 164
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (5, 7), deadline = 25
0.536225766734
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5362; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: -40.7426722466
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -40.74267224664739, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.74)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: -10.8575082957
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -10.857508295737182, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.86)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.92536938011
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.925369380106411, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.93)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 1.18627596868
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.186275968682533, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.19)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, 'left')
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: 1.68281900022
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.682819000222632, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded 1.68)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: -39.1559828979
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -39.155982897896095, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.16)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', 'right')
2.21877474155
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.59534568629
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.5953456862907418, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.60)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: -9.3387106732
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -9.338710673197768, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.34)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: -10.7495284144
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -10.749528414421624, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.75)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', 'right')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: 1.12301653451
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.123016534512819, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent drove right instead of forward. (rewarded 1.12)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: forward, reward: 1.13699006858
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.1369900685841206, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 1.14)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: -20.9639174388
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': -20.963917438848178, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.96)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, 'left')
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.61260028015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.61260028014736, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.61)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'forward')
1.39600195651
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 2.4152876127
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.415287612697548, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.42)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: forward, reward: -9.0408296959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': -9.040829695901168, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.04)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 0.865698011838
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 0.8656980118378583, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.87)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
2.0747657177
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: left, reward: 0.737165578235
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 0.7371655782350666, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.74)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', 'left', None)
2.25244730397
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 0.733814072034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 17, 'action': None, 'reward': 0.7338140720342761, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.73)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 1.51253826035
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.5125382603517108, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.51)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
1.68584506701
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.29702446823
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 2.297024468229555, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.30)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 0.417865462447
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': 0.4178654624470626, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.42)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', None, None)
1.20465011503
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.31244657824
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 4, 't': 21, 'action': None, 'reward': 2.3124465782447894, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.31)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'right', 'right')
0.0
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: forward, reward: 0.382507960837
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'right'), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.38250796083720706, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'right')
Agent drove forward instead of left. (rewarded 0.38)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', 'right', 'right')
0.0
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.78061886701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'right'), 'deadline': 2, 't': 23, 'action': None, 'reward': 1.7806188670060883, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 1.78)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: -9.60005662359
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 24, 'action': 'left', 'reward': -9.600056623585791, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.60)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 165
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (5, 4), deadline = 30
0.534191975471
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5342; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', None)
1.64387102009
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 1.64337370517
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': 1.6433737051714643, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.64)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'left')
1.71866563669
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.86904775698
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.869047756978033, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.87)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 0.0147405428437
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.014740542843703852, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.01)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
1.75854834664
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: None, reward: 2.81892630054
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.818926300540514, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.82)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
1.40596564797
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: 1.48220396384
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': 1.4822039638446305, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.48)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -40.0230374383
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 25, 't': 5, 'action': 'left', 'reward': -40.02303743825841, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.02)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'right', 'right')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 0.953360025229
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 0.953360025229114, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent drove right instead of forward. (rewarded 0.95)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: -19.8339503665
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 23, 't': 7, 'action': 'right', 'reward': -19.83395036647482, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.83)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'left')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 1.32563971508
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.3256397150808992, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.33)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'right', 'right')
0.890309433503
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 1.24331091632
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'right'), 'deadline': 21, 't': 9, 'action': None, 'reward': 1.2433109163229483, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 1.24)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 1.16867076586
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 1.1686707658645397, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.17)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 0.243810382994
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 0.24381038299364033, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 0.24)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: 1.07634395637
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': 1.076343956372944, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.08)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: 0.504839465071
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 17, 't': 13, 'action': None, 'reward': 0.5048394650710131, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 0.50)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'left')
1.18939002006
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: left, reward: 1.08202353987
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 16, 't': 14, 'action': 'left', 'reward': 1.0820235398656564, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.08)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: 1.10802266381
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 1.1080226638060622, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.11)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: -10.0887917785
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': -10.08879177849573, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.09)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: left, reward: 1.53069964996
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 13, 't': 17, 'action': 'left', 'reward': 1.5306996499636587, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.53)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'right', 'right')
0.0570174806822
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: forward, reward: 1.00888171166
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'right'), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': 1.0088817116592659, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'right')
Agent drove forward instead of right. (rewarded 1.01)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
1.57200119039
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: None, reward: 1.87882886531
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 19, 'action': None, 'reward': 1.8788288653108682, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.88)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', None, 'forward')
1.55392393635
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: None, reward: 0.664969536049
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 10, 't': 20, 'action': None, 'reward': 0.664969536048692, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.66)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', None, 'right')
2.01710986387
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 1.32266657299
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 1.3226665729925655, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.32)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, None)
1.41651826888
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 1.33730350717
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 22, 'action': 'forward', 'reward': 1.3373035071731099, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.34)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, None)
1.72541502785
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.82541381397
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 23, 'action': None, 'reward': 1.8254138139702947, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.83)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, None)
1.77541442091
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.92054482997
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 24, 'action': None, 'reward': 1.9205448299736136, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.92)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'green', None, None)
1.37691088803
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: 0.519335004879
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 5, 't': 25, 'action': 'forward', 'reward': 0.5193350048788761, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.52)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: -10.8912956197
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 4, 't': 26, 'action': 'forward', 'reward': -10.891295619660264, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.89)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'red', 'forward', None)
1.4652837258
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: None, reward: 1.1625786096
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 3, 't': 27, 'action': None, 'reward': 1.1625786096015354, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.16)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'red', 'forward', None)
1.43514116775
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: 1.27955631617
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 2, 't': 28, 'action': 'right', 'reward': 1.2795563161702883, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.28)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: None, reward: -4.0761037983
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 29, 'action': None, 'reward': -4.076103798300124, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.08)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 166
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (6, 5), deadline = 30
0.53216589795
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5322; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: 1.00819148013
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.008191480126997, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.01)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.070987666
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: 1.32280990348
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': 1.3228099034772427, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.32)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
1.19689878474
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: forward, reward: 1.48670322878
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': 1.4867032287810023, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.49)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: -9.18061885007
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': 'left', 'reward': -9.180618850066487, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.18)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: -9.51535322329
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': -9.515353223288109, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.52)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
0.770901998943
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: right, reward: 1.55184149297
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 1.5518414929722328, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.55)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'forward')
1.44233884887
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: 0.719261062265
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 0.7192610622654249, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.72)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', 'forward')
1.25184210801
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 2.23148736925
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 2.2314873692507553, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 2.23)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'right', 'forward')
0.890118839425
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: 2.19478881487
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.1947888148698365, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 2.19)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
1.16613067637
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: 2.0062119108
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 2.0062119108041405, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.01)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'left')
1.13570677996
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: left, reward: 2.38171548074
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 20, 't': 10, 'action': 'left', 'reward': 2.3817154807365006, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.38)
63% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 167
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (4, 6), deadline = 20
0.530147504913
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5301; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'right')
1.08132671711
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 2.46097052503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.460970525028854, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.46)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
2.08936960834
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 2.41413940561
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.4141394056131644, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.41)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -39.4080478581
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -39.40804785805195, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.41)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.86023033244
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 2.63381724812
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.633817248116008, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.63)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: -20.4318350265
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': -20.431835026480694, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.43)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 2.50800191326
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.508001913257469, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.51)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'forward')
1.6132323498
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.55474902587
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.554749025873829, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.55)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: -9.76841047648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': -9.768410476482297, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.77)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: left, reward: -9.54322054044
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -9.54322054043952, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -9.54)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'right', None)
1.64362236263
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 1.33887540716
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.3388754071629818, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.34)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 168
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (3, 6), deadline = 20
0.528136767216
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5281; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: -10.0093093532
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -10.009309353157976, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.01)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.2075061104
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.2075061104030689, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.21)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'forward')
1.58399068784
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.31439304893
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.3143930489277222, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.31)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'left')
1.3607878508
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.93579054463
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.9357905446309036, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.94)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'right', None)
0.808264044064
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.46960971687
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.4696097168660445, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.47)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 1.70067761077
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.7006776107651267, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.70)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: -4.58911348448
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 14, 't': 6, 'action': None, 'reward': -4.589113484483891, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.59)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: left, reward: 2.58456978193
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.584569781927386, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.58)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
1.35734874196
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: right, reward: 1.805889184
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.8058891839955693, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.81)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: -4.24406816935
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 11, 't': 9, 'action': None, 'reward': -4.244068169351444, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.24)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: right, reward: 0.342951834736
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.34295183473648716, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 0.34)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', 'left')
2.23588028303
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: left, reward: 1.99856591776
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 1.998565917764711, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 2.00)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: 0.928618971017
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.9286189710172543, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 0.93)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: -0.0615344781129
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': -0.06153447811287138, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded -0.06)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
1.58617129358
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: right, reward: 1.30058151365
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.3005815136472958, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.30)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: right, reward: 0.553385441683
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.5533854416826085, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.55)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
1.89730205313
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: None, reward: 1.59417674407
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.5941767440704497, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.59)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: None, reward: -5.81586013221
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': -5.815860132209916, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.82)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, None)
1.34180100676
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: forward, reward: 0.953435258905
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 0.9534352589054864, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.95)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'left', None)
2.25175450698
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: 1.72045721128
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 1.7204572112822503, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.72)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 169
\-------------------------
Environment.reset(): Trial set up with start = (2, 4), destination = (5, 5), deadline = 20
0.526133655823
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5261; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'forward')
1.9056447846
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 2.60202890569
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.602028905686068, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.60)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
2.28873732359
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 2.07404298205
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.0740429820452437, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'left')
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: forward, reward: -9.96847517281
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -9.968475172805581, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.97)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
2.18139015282
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 1.60715357517
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.607153575171807, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.61)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 1.8496056284
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.849605628398701, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.85)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 1.62581019787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.625810197867797, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.63)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'forward')
1.38230432331
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 1.63578037437
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.6357803743665975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.64)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'right', None)
1.13893688047
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.28222280016
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.282222800160251, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.28)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.7457393986
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.71737007186
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.71737007186007, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.72)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.73155473523
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.71726597214
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.7172659721361736, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.72)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 0.95913116858
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 0.9591311685799699, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.96)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.14761813283
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 0.832711342459
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 0.8327113424593717, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.83)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: left, reward: -0.106311370784
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -0.10631137078442099, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded -0.11)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, 'forward')
1.1094467362
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.79856841007
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.7985684100717763, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.80)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
1.84797962544
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 0.898604592571
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.898604592570625, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.90)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.37329210901
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.21830270748
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.218302707482918, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.22)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
1.29579740824
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.14927399229
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.1492739922884878, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.15)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', 'forward')
1.74166473863
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 2.0322174941
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 2.032217494104862, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 2.03)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'left')
1.84048907786
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 1.76316857098
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 1.7631685709844367, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.76)
5% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 170
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (4, 6), deadline = 25
0.524138141808
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5241; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.9225084452
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.9225084451975363, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.92)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 0.904431603832
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.9044316038322502, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent drove right instead of forward. (rewarded 0.90)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 0.603180762923
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.6031807629230381, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.60)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: -10.2769370371
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': -10.276937037140163, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.28)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.4457311145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.4457311145030902, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.45)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
1.45565135054
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: 2.84896583051
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 2.8489658305107675, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.85)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: -5.47745896673
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': -5.477458966729256, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.48)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: -9.61089184913
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -9.610891849130573, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.61)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: -39.9173795025
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -39.91737950248523, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.92)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: -0.133357104843
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': -0.1333571048429527, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded -0.13)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.3190547857
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.3190547856965018, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.32)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: -10.927429511
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': -10.927429510972502, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.93)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', 'forward')
1.29202107042
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 2.45537850819
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.4553785081876462, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.46)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
2.15230859053
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: left, reward: 1.44878811392
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 1.448788113917355, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.45)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'forward', None)
2.04198769574
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: None, reward: 2.34291577941
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 11, 't': 14, 'action': None, 'reward': 2.3429157794149758, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.34)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', None)
2.19245173758
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: None, reward: 2.47942621069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 2.4794262106886347, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.48)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'forward', 'right')
0.945622316125
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: forward, reward: 0.588396995451
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 0.5883969954507288, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove forward instead of left. (rewarded 0.59)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'right')
2.30468649464
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 2.09711733696
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 8, 't': 17, 'action': None, 'reward': 2.0971173369564156, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.10)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
1.8719387462
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 1.62934624681
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.629346246814671, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.63)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: left, reward: 2.26938537053
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': 2.2693853705250655, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.27)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', None, 'forward')
1.50904234884
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 1.81230660194
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': 1.8123066019403178, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.81)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: 0.818253721926
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 0.8182537219257703, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.82)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: 0.37448457731
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.3744845773095542, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.37)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', None, 'left')
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 1.56446330363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.5644633036283537, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.56)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: None, reward: -5.16408362177
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 24, 'action': None, 'reward': -5.164083621772379, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.16)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 171
\-------------------------
Environment.reset(): Trial set up with start = (6, 3), destination = (8, 5), deadline = 20
0.522150196358
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5222; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: -9.96378686223
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -9.963786862233857, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.96)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
1.98610585913
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 2.61020950039
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.610209500394544, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.61)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 1.41261956973
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.4126195697329502, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.41)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, 'right')
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: -5.92664354016
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 17, 't': 3, 'action': None, 'reward': -5.92664354016209, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.93)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
1.44337640362
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: right, reward: 2.56055455559
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 2.560554555585603, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.56)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'forward')
1.53664702256
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: 2.86956583604
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.86956583604319, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.87)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 172
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (8, 4), deadline = 30
0.520169790766
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5202; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: left, reward: -19.5876500607
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 30, 't': 0, 'action': 'left', 'reward': -19.587650060682776, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.59)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: 1.52752738547
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': 1.5275273854651945, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 1.53)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 1.04760054301
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.0476005430082864, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.05)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: forward, reward: 1.5686055709
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 1.568605570902562, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.57)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 0.757669795367
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 0.7576697953674586, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.76)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: right, reward: 1.6774156916
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 1.6774156915951193, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.68)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: -5.83427212306
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 6, 'action': None, 'reward': -5.83427212306272, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.83)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.75064249651
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 1.15816503877
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.1581650387661306, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.16)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: forward, reward: -10.4166068163
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': -10.416606816319328, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.42)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.76479987583
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: left, reward: 1.77422320689
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 1.774223206894866, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.77)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'right', 'forward')
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: None, reward: 1.24038871356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.2403887135596048, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.24)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: left, reward: 1.71864507189
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': 1.7186450718902093, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.72)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: left, reward: -39.5182831626
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 18, 't': 12, 'action': 'left', 'reward': -39.518283162591025, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.52)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, 'right')
1.66988821843
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: right, reward: 2.28836375504
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 2.2883637550407276, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 2.29)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: left, reward: 1.28602437828
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 14, 'action': 'left', 'reward': 1.2860243782805618, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.29)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'forward', 'left')
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: None, reward: -5.0981901936
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 15, 't': 15, 'action': None, 'reward': -5.098190193602385, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.10)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'left')
1.41472410209
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: right, reward: 1.6551538705
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 1.655153870496327, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.66)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: None, reward: -5.32358803131
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 13, 't': 17, 'action': None, 'reward': -5.323588031307761, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.32)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, 'forward')
1.7886463906
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: left, reward: 1.59073430104
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 12, 't': 18, 'action': 'left', 'reward': 1.590734301043665, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.59)
37% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 173
\-------------------------
Environment.reset(): Trial set up with start = (8, 6), destination = (5, 7), deadline = 20
0.518196896434
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5182; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: -9.84009938206
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -9.840099382058456, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent attempted driving left through a red light. (rewarded -9.84)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
1.69176975857
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.20905801876
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.2090580187648934, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.21)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.86792930535
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.98964151416
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.989641514158826, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.99)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.92878540975
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.5575313704
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.5575313703993543, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.56)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
1.74315839008
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.26510101198
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.2651010119797341, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.27)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: -9.79218901572
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': -9.792189015723583, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.79)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.45003048628
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.4500304862788436, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.45)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: -0.0317436097164
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': -0.03174360971639811, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded -0.03)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'left', None)
2.29815767976
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 1.08268868399
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.0826886839875451, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.08)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'right')
1.97912598674
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 1.22789878254
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.2278987825374026, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.23)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
1.493130688
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 1.32938859914
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.3293885991445007, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.33)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 1.44878334697
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.4487833469682563, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.45)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 0.816806164422
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.8168061644222646, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.82)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 0.714982492531
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.7149824925309124, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.71)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
1.50412970103
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 0.738202346079
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.7382023460793794, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.74)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
1.12116602355
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.05235889545
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 2.0523588954485583, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.05)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
1.80054835222
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: left, reward: 1.61030351184
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 1.6103035118404212, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.61)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
1.59177076113
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 0.75570070734
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 0.7557007073398023, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.76)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
1.17373573424
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 0.563118080044
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.5631180800442586, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.56)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: right, reward: 0.601063190008
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 0.6010631900075488, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, 'right')
Agent drove right instead of forward. (rewarded 0.60)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 174
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (2, 7), deadline = 30
0.516231484874
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5162; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', None)
1.5867624595
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 1.63016288831
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.6301628883145836, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.63)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
1.60846267391
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 1.45202305819
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.4520230581862812, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.45)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: right, reward: 0.653025131671
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.6530251316708994, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.65)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
2.14940007032
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: forward, reward: 1.70529152767
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 1.7052915276727476, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.71)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.03694427774
Environment.act() [POST]: location: (4, 4), heading: (0, -1), action: right, reward: 0.925646447829
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 0.9256464478294114, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.93)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: right, reward: 0.423124162085
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 0.423124162085054, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.42)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.76951154136
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: 1.65509596856
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 1.6550959685569786, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.66)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', None)
1.95041388867
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.37940206072
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.379402060718105, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.38)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: right, reward: 1.51651878067
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.5165187806712268, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.52)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
0.981295362782
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: right, reward: 0.656195040527
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 0.6561950405266883, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.66)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
1.71230375496
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: left, reward: 2.33692361169
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 10, 'action': 'left', 'reward': 2.336923611693104, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.34)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
0.86842690714
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 1.96347236515
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 11, 'action': None, 'reward': 1.9634723651501673, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.96)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
1.41594963615
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 1.7030998527
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.7030998526954995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.70)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.20264865784
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: left, reward: 0.777824301666
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'left', 'reward': 0.777824301665752, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.78)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'right', None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: -0.0692418327866
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': -0.0692418327866342, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent drove forward instead of right. (rewarded -0.07)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: 0.275101074249
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 0.2751010742486627, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 0.28)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'left', None)
1.69042318187
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 1.15009710973
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 1.1500971097270984, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.15)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.25928570203
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 13, 't': 17, 'action': None, 'reward': 1.2592857020297927, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.26)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: -40.315007592
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': -40.31500759200513, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.32)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', 'left', None)
1.927345799
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 1.00400846156
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 19, 'action': 'forward', 'reward': 1.0040084615599478, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.00)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 0.786172659017
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 20, 'action': 'right', 'reward': 0.7861726590165012, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.79)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'left', None)
1.70542593203
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: 0.631210211062
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 9, 't': 21, 'action': 'left', 'reward': 0.6312102110620044, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.63)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
2.02461368333
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: 0.792901507691
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 22, 'action': 'left', 'reward': 0.7929015076908699, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.79)
23% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 175
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (5, 2), deadline = 20
0.514273527707
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5143; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'right')
1.60351238464
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 2.09607339513
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.096073395127794, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 2.10)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'left')
2.29385669684
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.08076497408
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.080764974075844, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.08)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.12341382985
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 2.32048885211
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.3204888521064393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.32)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -10.9148208562
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -10.914820856154321, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.91)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: -10.4014153639
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.40141536390684, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.40)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 2.44936480162
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.44936480161947, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.45)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left')
1.39313814757
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 2.23393623716
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.233936237155522, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.23)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.51511102121
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.5151110212137398, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.52)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
1.53024286605
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.74875509372
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.7487550937246208, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.75)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', 'right')
0.470905256216
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 0.214600078165
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 0.2146000781649502, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 0.21)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
1.40875759551
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: 0.944079287899
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 0.9440792878989461, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.94)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 1.89994270895
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.899942708951934, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.90)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: right, reward: 1.30132710916
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.3013271091608953, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.30)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: left, reward: 1.78806681907
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.7880668190663198, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.79)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
1.63949897989
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 2.27436207694
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.2743620769361383, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.27)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
1.95693052841
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 1.14350071294
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.1435007129358266, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.14)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', 'right')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: -0.580437936978
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 4, 't': 16, 'action': 'right', 'reward': -0.5804379369780996, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove right instead of left. (rewarded -0.58)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: -4.02942062501
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': -4.02942062501299, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.03)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'left', None)
1.9867830747
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.21421817911
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.214218179108283, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.21)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 1.33443310563
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.3344331056329106, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.33)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 176
\-------------------------
Environment.reset(): Trial set up with start = (4, 4), destination = (1, 6), deadline = 25
0.512322996657
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
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epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
Simulating trial. . .
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Simulating trial. . .
epsilon = 0.5123; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'left')
2.1172231004
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: 1.43165915662
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.4316591566177563, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 1.43)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
0.990236479752
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: left, reward: 1.68882305253
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 1.6888230525346897, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.69)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 2.27169623052
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.2716962305219637, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.27)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
1.79281369911
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 2.14104585385
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.1410458538539907, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.14)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', None)
1.96692977648
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.74972671177
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.7497267117696067, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.75)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'forward', None)
1.85832824413
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.66407662238
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.6640766223848744, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.66)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: forward, reward: 1.78731333644
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.7873133364354241, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 1.79)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: forward, reward: 0.00561906831467
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 0.005619068314667741, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded 0.01)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 2.18601942385
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.186019423846899, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.19)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None)
1.27848440295
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 2.42417985912
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.4241798591165864, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.42)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: forward, reward: 0.255038133062
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 0.25503813306210243, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 0.26)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
2.0019654796
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 0.825711462614
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 0.8257114626140103, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.83)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.33952976614
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: 1.43909712233
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 1.4390971223305509, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.44)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, 'forward')
1.81131437747
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 2.24096355343
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 2.240963553431828, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.24)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: 2.52826426138
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 2.5282642613827346, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.53)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
1.72195134098
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 0.794912568833
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 0.7949125688330898, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.79)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
1.95752096595
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: forward, reward: 1.35224185764
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 1.3522418576419106, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.35)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: forward, reward: -40.7700872167
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': -40.770087216651476, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.77)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', 'left')
0.968650111862
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 1.01299259042
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.0129925904243744, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent drove right instead of forward. (rewarded 1.01)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
1.69235005169
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 1.36183706636
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.3618370663640305, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.36)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: -39.0708151226
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': -39.07081512263811, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.07)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None)
1.1764184417
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: 1.13425887181
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 1.1342588718113924, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.13)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: 0.591426335083
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.5914263350834605, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.59)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'left', 'forward')
1.56267206899
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: 1.38130469128
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': 1.381304691284597, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.38)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: -10.7696178693
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': -10.769617869342312, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.77)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 177
\-------------------------
Environment.reset(): Trial set up with start = (4, 6), destination = (1, 7), deadline = 20
0.510379863561
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5104; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: 1.52896074065
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.5289607406539416, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.53)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 1.18072512596
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.1807251259587235, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.18)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: -10.3905739668
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.390573966820375, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.39)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None)
1.41383847111
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: right, reward: 2.69018351312
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.69018351312154, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.69)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'forward')
1.60423878521
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 2.90873432019
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.9087343201894154, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.91)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: right, reward: -0.0559372819633
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': -0.05593728196329484, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.06)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.15533865676
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: left, reward: 2.41631359893
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 2.416313598927439, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.42)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', 'left')
1.52661747781
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: forward, reward: 2.23114039002
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.2311403900220066, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 2.23)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.52709355903
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 2.27827630717
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.2782763071668573, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.28)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: right, reward: 0.694796934512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.6947969345119105, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.69)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'right', None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: forward, reward: 1.40758947413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.4075894741348, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent drove forward instead of right. (rewarded 1.41)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'right', 'forward')
1.3561234543
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: 2.61456637154
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 2.614566371540831, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 2.61)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: -9.32711504343
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -9.327115043431316, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.33)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
1.6548814118
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: forward, reward: 0.76086578739
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.760865787389664, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.76)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', None)
2.24702379028
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 2.01178384217
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.0117838421658254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.01)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
2.12940381622
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.80959224612
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.8095922461169567, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.81)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
1.20787359959
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: forward, reward: 0.424617506001
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.4246175060013342, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.42)
15% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 178
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (1, 6), deadline = 25
0.508444100359
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5084; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 2.98714659469
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.987146594688209, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.99)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
1.96949803117
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 1.46482380665
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.4648238066512655, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.46)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: -10.6681257826
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -10.668125782631067, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.67)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.71716091891
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 1.22667258128
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.2266725812811645, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.23)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 2.48805540067
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.4880554006708753, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.49)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 0.947953666943
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 0.9479536669432385, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.95)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: left, reward: -9.05918012787
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -9.059180127867776, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -9.06)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'right')
1.89145868263
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 1.73538082435
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.7353808243488684, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.74)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.25373920568
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 2.80864328342
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.808643283421731, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.81)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 2.09603823936
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.0960382393570978, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.10)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
1.9026849331
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.46437195302
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.4643719530246875, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.46)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
2.18352844306
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 1.87334908558
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.8733490855847632, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.87)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
1.66490797469
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 1.78838205954
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.788382059542288, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.79)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
1.16137174596
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: right, reward: -0.103821022009
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': -0.10382102200931431, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded -0.10)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', 'left')
0.990821351143
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: -0.0601474584979
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': -0.060147458497878614, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent drove right instead of forward. (rewarded -0.06)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'forward')
1.68969034582
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: 2.49678129721
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 2.4967812972063, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.50)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: -40.4412646709
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': -40.4412646708673, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.44)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: None, reward: 0.758642142298
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 8, 't': 17, 'action': None, 'reward': 0.7586421422979985, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 0.76)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
1.55021562067
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: None, reward: 2.06255962836
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 7, 't': 18, 'action': None, 'reward': 2.0625596283625773, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.06)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 0.267033344682
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 0.2670333446818004, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.27)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
2.02843876432
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.31049523449
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': 2.310495234491376, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.31)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None)
1.78582612784
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 0.850890308097
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 0.8508903080971575, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.85)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
1.31835821797
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: 2.23132097743
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 2.231320977428738, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.23)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.62059085498
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 2, 't': 23, 'action': None, 'reward': 1.6205908549814387, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.62)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'forward', None)
1.4719167501
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 0.146028374145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 1, 't': 24, 'action': None, 'reward': 0.1460283741451438, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.15)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 179
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (6, 3), deadline = 30
0.506515679099
Simulating trial. . .
epsilon = 0.5065; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5065; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5065; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5065; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5065; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: -20.3331953341
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': -20.33319533414844, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.33)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 1.22415407958
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.2241540795848467, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.22)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
1.4202601458
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: right, reward: 1.1125791524
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 1.1125791523975515, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.11)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
1.70117508467
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 1.70567551347
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 1.7056755134661534, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.71)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 2.50829466196
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.5082946619633537, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.51)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
1.65863361204
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.15735203882
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.157352038820965, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.16)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: left, reward: -20.575645514
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': -20.57564551397863, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.58)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.38931344424
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: left, reward: 0.687642678768
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 0.6876426787682776, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.69)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
1.70342529907
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 1.8754168586
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.8754168586014524, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.88)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 0.960790277411
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 0.960790277410777, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.96)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: -0.0915282623582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': -0.0915282623581879, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded -0.09)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -39.673789697
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 19, 't': 11, 'action': 'left', 'reward': -39.673789697018734, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.67)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 1.45313587773
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.4531358777264647, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.45)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.24567104826
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 13, 'action': None, 'reward': 2.245671048261388, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.25)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, 'left')
1.56187933207
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 1.6096949101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.6096949101007612, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.61)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -10.3641707881
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -10.36417078809608, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.36)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 1.66209325256
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 1.662093252563237, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.66)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, 'forward')
2.02613896545
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 1.756718431
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 1.7567184310048038, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.76)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: 1.81159964478
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': 1.8115996447832332, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.81)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', 'left')
1.68731083546
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 2.13183631075
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.131836310752549, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.13)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', 'left')
1.9095735731
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.60261996358
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 10, 't': 20, 'action': None, 'reward': 1.6026199635771208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.60)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', 'left', None)
1.2584319549
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 2.34690235664
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 9, 't': 21, 'action': None, 'reward': 2.3469023566351774, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.35)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'left', None)
1.03348185065
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: left, reward: 0.662577237601
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 8, 't': 22, 'action': 'left', 'reward': 0.6625772376005323, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.66)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: -4.46247126217
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 7, 't': 23, 'action': None, 'reward': -4.462471262169478, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.46)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, None)
1.85705212234
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: 2.34955408429
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 24, 'action': 'right', 'reward': 2.3495540842883518, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.35)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('right', 'red', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: -9.07525967835
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 5, 't': 25, 'action': 'forward', 'reward': -9.075259678354513, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.08)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'red', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: left, reward: -9.08005252839
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 4, 't': 26, 'action': 'left', 'reward': -9.080052528391208, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.08)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'green', 'left', 'forward')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 0.822069575976
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 3, 't': 27, 'action': 'right', 'reward': 0.822069575976393, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 0.82)
7% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 180
\-------------------------
Environment.reset(): Trial set up with start = (5, 6), destination = (1, 5), deadline = 25
0.504594571934
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5046; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', 'right')
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 1.90883142167
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'right'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.9088314216732511, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'right')
Agent followed the waypoint right. (rewarded 1.91)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
1.76120243326
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: None, reward: 2.96153142941
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.9615314294078297, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.96)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: -19.6882549392
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': -19.688254939178236, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.69)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'forward', 'left')
0.152333693534
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: forward, reward: 1.5711238787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 1.5711238786991597, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded 1.57)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
1.86512665069
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: None, reward: 2.4356521284
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.4356521283958266, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.44)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: left, reward: 0.249008462101
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 0.24900846210113736, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.25)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', 'left')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: 2.75176395953
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 2.751763959533, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 2.75)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', 'right')
1.90706021392
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 2.29065404026
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.2906540402620625, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.29)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -9.0805524145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -9.080552414501652, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.08)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: right, reward: 1.54707783378
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.5470778337764775, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent drove right instead of forward. (rewarded 1.55)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'left')
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: forward, reward: -10.5335264565
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': -10.533526456492956, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.53)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'left')
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: left, reward: -10.6169423161
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -10.616942316147508, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.62)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
2.16946699941
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: None, reward: 2.35174724671
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.351747246705534, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.35)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: forward, reward: -0.167016463095
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': -0.16701646309456653, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded -0.17)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 2.30497734474
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 11, 't': 14, 'action': None, 'reward': 2.304977344744416, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.30)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
2.05568248463
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 1.09423477088
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.0942347708847302, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.09)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: 0.937136352894
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 0.9371363528941026, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.94)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
1.0384780615
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: 0.944887089835
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': 0.9448870898352362, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.94)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None)
1.2664196491
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 0.792470047169
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 0.792470047168814, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 0.79)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, 'left')
1.81353719237
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 0.837095550526
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 6, 't': 19, 'action': None, 'reward': 0.8370955505255038, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.84)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, 'left')
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 0.630210971202
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 5, 't': 20, 'action': None, 'reward': 0.6302109712019199, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.63)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: 1.04312416482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 1.043124164822872, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.04)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: forward, reward: -10.1807819424
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': -10.180781942400582, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -10.18)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: forward, reward: -10.5393289225
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 2, 't': 23, 'action': 'forward', 'reward': -10.5393289225455, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.54)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', None, 'left')
1.64828919771
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.1698741015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 1, 't': 24, 'action': None, 'reward': 1.1698741014963092, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.17)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 181
\-------------------------
Environment.reset(): Trial set up with start = (4, 4), destination = (7, 2), deadline = 25
0.502680751123
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5027; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', None)
1.25873502654
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.02963542663
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.029635426629884, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.03)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: left, reward: -39.1206725791
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -39.12067257911302, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.12)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.93398175488
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.9339817548826055, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.93)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
2.28908349073
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.66046741966
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.6604674196615445, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.66)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'left', None)
1.9747754552
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.25772182605
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.2577218260501217, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.26)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
1.6005006269
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 2.58730963361
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 2.5873096336129207, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.59)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'right')
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: None, reward: -5.34565899781
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 19, 't': 6, 'action': None, 'reward': -5.34565899781392, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -5.35)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
2.10330310331
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: right, reward: 1.57634068964
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.5763406896361658, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.58)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 2.59698889178
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.596988891781238, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.60)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: left, reward: -10.3695858592
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -10.36958585918137, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.37)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: left, reward: -10.4220944886
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -10.42209448864275, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -10.42)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
0.977869011439
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: right, reward: -0.176280971691
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': -0.1762809716908601, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded -0.18)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: forward, reward: -9.95331156955
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': -9.953311569550484, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.95)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: forward, reward: 1.06324806507
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.0632480650747609, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.06)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: None, reward: 2.43108732464
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 11, 't': 14, 'action': None, 'reward': 2.4310873246365814, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.43)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: right, reward: 0.332100378474
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.33210037847393226, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove right instead of left. (rewarded 0.33)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'right')
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: left, reward: -9.03196342452
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': -9.03196342452393, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -9.03)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: left, reward: -20.1091658198
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 8, 't': 17, 'action': 'left', 'reward': -20.10916581978178, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.11)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: forward, reward: -0.461655869836
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -0.46165586983643614, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded -0.46)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, 'forward')
2.2564865527
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 1.89869961597
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.898699615971579, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.90)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None)
1.80266715577
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 1.45138904889
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.4513890488893926, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.45)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: 0.435707065181
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 0.43570706518106606, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.44)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'left', None)
1.2076643991
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: left, reward: 1.75946332092
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 1.7594633209179784, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.76)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, None)
2.03119124455
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 1.08949975185
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 2, 't': 23, 'action': None, 'reward': 1.0894997518476666, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.09)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: -10.111590406
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': -10.11159040601421, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.11)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 182
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (5, 3), deadline = 20
0.500774189031
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
Simulating trial. . .
epsilon = 0.5008; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'left')
0.700062779346
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 1.42862874679
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.4286287467872947, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.43)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', 'forward')
2.3099124588
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.1666155151
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.16661551509962, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.17)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
1.72664501712
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.70041989566
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.7004198956620225, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.70)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: -10.1831302018
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.183130201757892, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.18)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 1.2427175539
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.2427175539015836, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.24)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: 1.45653921052
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.4565392105187318, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.46)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: left, reward: 2.68329822538
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 2.683298225384744, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.68)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
2.07759308433
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 1.89797405887
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.897974058868897, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.90)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 1.45915921723
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.4591592172277208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.46)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 2.17758480414
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.177584804141749, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.18)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', 'left')
2.22619341675
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: 0.846003528458
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 0.8460035284575016, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 0.85)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
1.5603454982
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 2.27570620556
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.275706205561646, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.28)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
1.91802585188
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 2.07428919171
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.0742891917103354, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.17981389377
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.179813893769284, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.18)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', 'left')
0.995288848363
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: -0.101118464189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'left'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': -0.10111846418869652, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'left')
Agent drove right instead of left. (rewarded -0.10)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 0.031266115313
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.03126611531298207, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 0.03)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
2.0834310427
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: 1.03432290961
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 1.034322909606596, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.03)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: -39.7213280535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -39.721328053534656, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.72)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
2.26060712306
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 0.482387362261
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.4823873622606556, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.48)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, 'left')
1.75871113035
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: 1.00134402106
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 1.0013440210632862, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.00)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 183
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (7, 3), deadline = 20
0.498874858127
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4989; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: -39.5606811114
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -39.56068111142357, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.56)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: right, reward: 1.26577477339
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.2657747733857652, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.27)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', 'forward')
2.05867467403
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: None, reward: 2.70224087313
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.7022408731331353, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.70)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'left', None)
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: left, reward: 1.02104674139
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.0210467413928659, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded 1.02)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
2.15038938954
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.70230440544
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.7023044054390026, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.70)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, 'right')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: 1.17957300338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.179573003380226, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent drove forward instead of right. (rewarded 1.18)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'right')
1.81341975349
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 1.71318435896
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.713184358958763, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.71)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: right, reward: -0.1333141147
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': -0.13331411470010757, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.13)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
1.7748395977
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: left, reward: 1.00727237901
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 1.0072723790074378, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.01)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.86033923196
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: 1.41887338069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.4188733806886746, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.42)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: left, reward: -9.27422460834
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -9.274224608335416, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.27)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', 'right')
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: left, reward: 0.352961927579
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 0.35296192757851164, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent drove left instead of forward. (rewarded 0.35)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
2.42634689749
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 0.694750869588
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.6947508695884661, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.69)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
1.83982189647
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 0.883789669413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.8837896694133232, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.88)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: -4.00087616858
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': None, 'reward': -4.000876168576941, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.00)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'left')
1.80182882442
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 1.12488634179
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 1.1248863417927106, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.12)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: -39.0215009844
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -39.02150098439846, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.02)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', 'left', 'left')
1.75609676834
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.6967708781
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.6967708781032407, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.70)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: 0.665974081437
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.6659740814370556, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.67)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 1.43818565723
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.43818565722644, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.44)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 184
\-------------------------
Environment.reset(): Trial set up with start = (4, 3), destination = (8, 5), deadline = 30
0.496982730984
Simulating trial. . .
epsilon = 0.4970; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4970; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4970; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4970; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4970; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4970; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4970; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4970; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4970; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', None)
2.09390513026
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: right, reward: 1.1820191546
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.1820191546020156, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.18)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
1.36180578294
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 2.61216893342
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 2.6121689334161835, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.61)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
1.98058598956
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: 1.07972421331
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': 1.0797242133066474, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.08)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: -5.07378774424
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 27, 't': 3, 'action': None, 'reward': -5.0737877442393415, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.07)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
1.46335758311
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 2.5164646592
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 2.516464659204523, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.52)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 2.46803076524
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 2.468030765240408, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.47)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.39105598835
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: 2.3436682338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 2.3436682338012913, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.34)
77% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 185
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (1, 5), deadline = 20
0.495097780281
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4951; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'left')
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: right, reward: 0.60444878622
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 0.6044487862195234, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded 0.60)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'right')
2.05728528827
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: left, reward: 2.5501438926
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 2.550143892604159, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.55)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: left, reward: 1.50339751908
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': 1.503397519080033, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.50)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: left, reward: -40.5451764096
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -40.545176409599435, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.55)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.9961575218
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 1.32709075491
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.3270907549139666, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.33)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 1.70741282773
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.7074128277251244, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.71)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 1.55084842118
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.550848421175552, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.55)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'forward', None)
2.36136693133
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.38391121008
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.383911210082428, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.38)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: -9.97630127588
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.976301275880957, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.98)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'forward', None)
1.87263907071
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.62104375373
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.6210437537304325, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.62)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: left, reward: 0.104297971582
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 0.10429797158200593, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded 0.10)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'right', 'forward')
1.12267229366
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: None, reward: 0.841699844934
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.8416998449344522, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 0.84)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
1.37149724266
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: None, reward: 1.53263215567
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.532632155669055, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.53)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
1.86736211108
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: left, reward: 1.8578324033
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.8578324033048255, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.86)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: left, reward: -10.9056396529
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -10.905639652852448, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -10.91)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
1.63960630632
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 0.999282095729
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.9992820957285318, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.00)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 1.00381628799
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.0038162879940649, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 1.00)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: 1.5057465727
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 1.50574657270147, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.51)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: -4.32894103459
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': -4.328941034589287, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.33)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, None)
1.17119276999
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: -0.740975279023
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': -0.740975279022822, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded -0.74)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 186
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (5, 4), deadline = 20
0.493219978798
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4932; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'right')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: left, reward: -10.8210945352
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -10.821094535241063, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -10.82)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'left')
1.58578712108
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 1.6679215236
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.6679215235960672, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.67)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 2.70179517276
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.70179517276338, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.70)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 1.51245132012
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.5124513201247096, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.51)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'right')
2.2009019158
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: 1.20239983708
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.2023998370756301, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.20)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: -5.86897322
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': -5.868973220001723, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.87)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.86259725719
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: left, reward: 1.21471242363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 1.214712423628224, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.21)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: forward, reward: 1.80827458802
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.8082745880237638, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.81)
60% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 187
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (3, 4), deadline = 30
0.491349299421
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
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epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4913; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: -9.22796372124
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': -9.227963721235382, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.23)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: left, reward: -10.3676824922
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 29, 't': 1, 'action': 'left', 'reward': -10.367682492198625, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.37)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: -9.43945355873
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': -9.43945355873229, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.44)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'left')
1.40908164961
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 2.85448209281
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.8544820928105423, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.85)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 1.18036758419
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 1.1803675841937697, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.18)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'forward')
1.9877835716
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 1.79109670096
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.791096700963347, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.79)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: -9.66116575848
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': -9.661165758481197, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.66)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', 'right')
0.417759193185
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 0.87135107063
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 0.8713510706297006, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent drove right instead of forward. (rewarded 0.87)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', 'forward')
1.83822482209
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 0.974310718514
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 22, 't': 8, 'action': None, 'reward': 0.9743107185138842, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 0.97)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
2.21353245639
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 2.18243009631
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.1824300963080905, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.18)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', 'right')
0.93676261661
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 0.344885543686
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 0.34488554368616375, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent drove right instead of left. (rewarded 0.34)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'left')
0.977763671324
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.84198696612
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 19, 't': 11, 'action': None, 'reward': 2.8419869661231996, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.84)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
1.45206469916
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.51758955276
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': 2.5175895527625585, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.52)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'left', None)
1.57495862776
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.93123327123
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 13, 'action': None, 'reward': 1.931233271233971, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.93)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 0.297242227295
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 0.297242227295339, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.30)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'left')
1.5349389863
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.40227443286
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 15, 't': 15, 'action': 'right', 'reward': 1.402274432864618, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.40)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 1.11610534138
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 1.116105341379154, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.12)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'right', None)
2.02005639256
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 2.24605530521
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 13, 't': 17, 'action': None, 'reward': 2.246055305206724, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.25)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: left, reward: -39.3887972256
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 12, 't': 18, 'action': 'left', 'reward': -39.388797225608116, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.39)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
1.98482712596
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 2.35176722062
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.351767220616506, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.35)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
2.16829717329
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 1.76202146183
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 20, 'action': None, 'reward': 1.7620214618315975, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.76)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: -0.401374018702
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': -0.40137401870216516, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded -0.40)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'red', None, None)
1.96515931756
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.23260877368
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 22, 'action': None, 'reward': 1.2326087736827886, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.23)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: -10.3641730897
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 23, 'action': 'forward', 'reward': -10.364173089747236, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.36)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: left, reward: -9.24418105321
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 24, 'action': 'left', 'reward': -9.244181053210616, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.24)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'red', 'left', None)
1.7530959495
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.2025469306
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 25, 'action': None, 'reward': 1.2025469306021535, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.20)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', 'left', 'forward')
1.47198838014
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: 1.64093436369
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 4, 't': 26, 'action': 'left', 'reward': 1.6409343636941953, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.64)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'red', None, None)
1.59888404562
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 1.76612913762
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 3, 't': 27, 'action': None, 'reward': 1.7661291376228163, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.77)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: -10.5263536319
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 28, 'action': 'forward', 'reward': -10.526353631924483, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.53)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('left', 'green', None, 'right')
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: forward, reward: -0.176454814732
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 1, 't': 29, 'action': 'forward', 'reward': -0.17645481473247293, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, 'right')
Agent drove forward instead of left. (rewarded -0.18)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 188
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (2, 3), deadline = 35
0.489485715136
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4895; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
1.53865484041
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 1.03988649774
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 35, 't': 0, 'action': 'left', 'reward': 1.039886497740329, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.04)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 0.854972859316
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 34, 't': 1, 'action': 'forward', 'reward': 0.8549728593161279, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove forward instead of left. (rewarded 0.85)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.47782144005
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 2.01231854955
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 33, 't': 2, 'action': None, 'reward': 2.0123185495479454, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.01)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.7450699948
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 1.77470275967
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 32, 't': 3, 'action': None, 'reward': 1.774702759671983, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.77)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
1.75988637724
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 2.74025715074
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 31, 't': 4, 'action': None, 'reward': 2.74025715074269, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.74)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: left, reward: 2.04905287496
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 30, 't': 5, 'action': 'left', 'reward': 2.0490528749571664, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.05)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'forward')
1.88944013628
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 2.55888508384
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 29, 't': 6, 'action': None, 'reward': 2.558885083840625, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.56)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: forward, reward: -9.63023888711
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 28, 't': 7, 'action': 'forward', 'reward': -9.630238887108908, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.63)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: right, reward: 1.04524015713
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 27, 't': 8, 'action': 'right', 'reward': 1.0452401571341312, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.05)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.28927066907
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: left, reward: 1.00230792193
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 26, 't': 9, 'action': 'left', 'reward': 1.0023079219309075, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.00)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: right, reward: 1.50587477851
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 10, 'action': 'right', 'reward': 1.505874778507493, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.51)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
1.80396492555
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: 2.70363405542
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 24, 't': 11, 'action': 'left', 'reward': 2.7036340554180365, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.70)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'left')
1.63014008353
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 2.8122248723
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 23, 't': 12, 'action': 'forward', 'reward': 2.812224872296176, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.81)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: 1.26073916467
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 22, 't': 13, 'action': 'forward', 'reward': 1.2607391646711763, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.26)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: -5.98953379804
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 14, 'action': None, 'reward': -5.9895337980403625, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.99)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
1.18438809544
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: right, reward: 0.382652916284
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 15, 'action': 'right', 'reward': 0.38265291628351183, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.38)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
1.98698735818
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: right, reward: 1.24773164834
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 16, 'action': 'right', 'reward': 1.2477316483428698, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.25)
51% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'forward')
1.82001349849
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.27494467551
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 18, 't': 17, 'action': None, 'reward': 1.2749446755082885, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.27)
49% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
2.2456081258
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 2.34383423491
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 18, 'action': 'right', 'reward': 2.3438342349144774, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.34)
46% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, None)
1.31349976803
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: forward, reward: 2.63586265675
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 19, 'action': 'forward', 'reward': 2.635862656754542, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.64)
43% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 189
\-------------------------
Environment.reset(): Trial set up with start = (6, 7), destination = (8, 3), deadline = 20
0.487629199033
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4876; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: forward, reward: -9.5035087371
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -9.503508737097423, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.50)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.68250659162
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 2.30709573427
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.307095734274852, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.31)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'right')
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: forward, reward: -40.4887373255
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -40.48873732548393, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.49)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
1.1457892955
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: left, reward: 2.47909585162
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 2.4790958516157025, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.48)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: forward, reward: -9.69188450259
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -9.691884502586635, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.69)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
1.97468121239
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: 0.919920524084
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 0.9199205240838866, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.92)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'left')
1.62685432234
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.73415252977
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.73415252977206, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.73)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'left')
1.46860670958
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 2.25251160915
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 2.252511609150636, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.25)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
1.31944420103
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 0.974729059356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.974729059355524, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.97)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.14708663019
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.51314689732
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.5131468973181303, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.51)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
1.33011676375
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 2.29377941286
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.2937794128585525, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.29)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 0.353389388437
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.35338938843668943, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent drove right instead of forward. (rewarded 0.35)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -10.2829464098
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -10.282946409759237, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.28)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 1.52357028521
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.5235702852107487, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.52)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
2.29472118036
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 1.16681968514
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.1668196851418096, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.17)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None)
1.61735950326
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 0.817711471675
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.8177114716754894, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.82)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'forward')
2.22416261006
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 0.856399755575
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': 0.8563997555754059, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.86)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
1.66162413835
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.85694652645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.8569465264495653, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.86)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'right', None)
1.21057984031
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.04913802821
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.0491380282057678, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.05)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, 'forward')
1.54028118282
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.80970116051
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.8097011605064541, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.81)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 190
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (5, 3), deadline = 20
0.485779724304
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4858; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', None)
1.20484100679
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: 1.93929729943
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.939297299426067, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.94)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'forward', 'forward')
1.88694111637
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: right, reward: 1.49545765736
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.4954576573604057, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 1.50)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
2.22118247791
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: 1.93812674186
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.9381267418571761, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.94)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: -10.9844070122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.984407012173339, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.98)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
2.19798127635
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 1.64203260354
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.6420326035412534, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.64)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: -4.34067799574
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': -4.34067799573634, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.34)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: right, reward: 0.944496287282
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.9444962872822249, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.94)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
1.73077043275
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: right, reward: 2.46071979694
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 2.460719796943059, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.46)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'left')
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: left, reward: -0.0584794981757
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -0.058479498175719846, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded -0.06)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
2.25379949049
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: left, reward: 0.954925999438
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 0.9549259994379957, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.95)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'right', None)
1.94754117096
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: right, reward: 2.22189676567
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 2.2218967656717514, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 2.22)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'right', None)
1.12985893426
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 0.779831189452
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.779831189451963, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.78)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', 'right')
0.644555131907
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: right, reward: 0.386735444247
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 0.386735444246755, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent drove right instead of forward. (rewarded 0.39)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: left, reward: -20.5891228213
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -20.58912282132405, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.59)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', None)
1.60436274496
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: 0.565245490026
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 0.5652454900262507, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.57)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: -20.2107690695
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -20.21076906945786, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.21)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'left')
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.03058424117
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.0305842411699369, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.03)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'left')
1.47022977995
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 2.31733853904
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 3, 't': 17, 'action': None, 'reward': 2.317338539044851, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.32)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
2.25007176399
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 0.974193818438
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.9741938184378933, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.97)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: forward, reward: 0.558399370052
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': 0.5583993700517346, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.56)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 191
\-------------------------
Environment.reset(): Trial set up with start = (1, 4), destination = (3, 6), deadline = 20
0.483937264243
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4839; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'forward')
1.547479087
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: 1.0227389561
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.0227389561023523, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.02)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: 1.82127150706
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.821271507064444, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.82)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: 1.5455712774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.5455712773955297, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.55)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
2.09574511485
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: right, reward: 1.16666568816
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.1666656881610096, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.17)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.7592853324
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.49947613948
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.499476139478171, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.50)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
1.44730086824
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: 1.19956616712
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.19956616712151, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.20)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None)
1.63796214243
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: right, reward: 1.05739592266
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.0573959226643184, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.06)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: right, reward: 1.32336798641
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.323367986405235, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.32)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
1.44171254739
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: left, reward: 1.48847341753
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 1.4884734175338938, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.49)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: 1.08586491176
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.0858649117611308, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.09)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
1.54033303735
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: 2.21695315188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.216953151884745, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.22)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: -5.12335139417
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': -5.123351394173383, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.12)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: forward, reward: -0.101136798336
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -0.10113679833551537, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded -0.10)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'right', 'right')
0.191253980419
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: -0.0816874444816
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'right'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -0.08168744448158594, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'right')
Agent drove forward instead of left. (rewarded -0.08)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: -9.31216676478
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -9.312166764775569, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.31)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: left, reward: -9.89184138621
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -9.891841386207238, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.89)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: -9.37504671771
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -9.375046717707402, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.38)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
1.81244257356
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: left, reward: 1.86009054563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 1.8600905456269456, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.86)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', 'right', None)
0.150502944821
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 1.06023679375
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.0602367937460184, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.06)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'right', 'left')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: -9.48504008004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': -9.48504008004165, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'right', 'left')
Agent attempted driving forward through a red light. (rewarded -9.49)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 192
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (3, 7), deadline = 25
0.482101792244
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4821; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: -20.6002381611
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': -20.600238161109598, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.60)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.87864309462
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 2.20898437726
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.208984377255084, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.21)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: left, reward: -9.78648416468
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -9.786484164675814, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.79)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 1.93255208902
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.9325520890207322, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.93)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'right', 'left')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: left, reward: -19.2011827478
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -19.2011827477601, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.20)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
2.13178187121
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.32838456304
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.328384563041447, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.33)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: -39.0981030891
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': -39.09810308905348, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.10)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'right', None)
0.954845061856
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.08444453915
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.084444539146876, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.08)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', None)
1.5196448005
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.4525024022
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.4525024022032067, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.45)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 1.237031265
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.237031264995032, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.24)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: 1.83502829648
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.8350282964823112, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.84)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: forward, reward: 1.104415512
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 1.1044155119988959, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.10)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
2.12938073594
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: None, reward: 1.57007917842
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.570079178423909, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.57)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.57923090708
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: forward, reward: 2.42699077621
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 2.4269907762060505, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.43)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'forward', 'forward')
1.69119938686
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: 1.04971276891
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.049712768910815, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 1.05)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
2.00249085861
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 0.684427158869
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 0.6844271588688211, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.68)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'forward', None)
1.34345900874
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 1.6274272079
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.6274272078990606, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.63)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'right', None)
1.4912488849
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: forward, reward: 1.28336773482
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.2833677348249577, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.28)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 193
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (2, 3), deadline = 20
0.480273281804
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4803; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'left')
1.06434576307
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.31912926975
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.319129269751389, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.32)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 2.12546187688
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.1254618768757894, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.13)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
1.92000693995
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 2.88617337857
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.8861733785724315, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.89)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: -9.79599807399
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -9.795998073992303, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.80)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 0.941569079045
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 0.9415690790454612, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.94)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', 'left')
1.19173751641
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.99742601077
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.997426010767501, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.00)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
1.18564635975
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.85298323956
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.8529832395556283, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.85)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 1.18960658562
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.189606585623794, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.19)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.03211924078
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.032119240777126, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.03)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'left', None)
1.34767903255
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 2.72422923819
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 2.7242292381905933, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.72)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', None)
1.73390617396
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 0.947207722649
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 0.9472077226490558, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 0.95)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 1.43529509141
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.4352950914077214, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.44)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: 1.49291883531
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 1.4929188353119585, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.49)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
1.83626655959
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: 0.776163455744
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 0.7761634557436716, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.78)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 194
\-------------------------
Environment.reset(): Trial set up with start = (1, 3), destination = (4, 4), deadline = 20
0.478451706518
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4785; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: forward, reward: -10.3385995291
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -10.338599529144638, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.34)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
1.61213279121
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.40550113791
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.4055011379075313, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.41)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: -10.1182998774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.118299877435607, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.12)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.50881696456
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 2.27893955936
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.278939559363186, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.28)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
1.51931479965
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 0.535600631312
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 0.5356006313124019, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.54)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 0.122466397983
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 0.12246639798290504, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.12)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: -9.40757558736
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -9.407575587355822, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.41)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.70116999886
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.7011699988572415, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.70)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'left', None)
2.11624864062
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.43366986188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.4336698618847283, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.43)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: left, reward: -39.4389437503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -39.438943750327624, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.44)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None)
1.77495925125
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.92290507012
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.922905070116837, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.92)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', 'forward')
1.23488096466
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 2.18651244735
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.1865124473512583, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 2.19)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -10.8627782802
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -10.862778280211876, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.86)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -9.62574367713
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -9.62574367713251, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.63)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: -40.584745294
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -40.58474529399393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.58)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
1.48544310832
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 1.61379396016
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.613793960157794, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.61)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -20.7602775112
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -20.760277511248034, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.76)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 0.155427614087
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.15542761408664207, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.16)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
2.04381373594
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: None, reward: 0.931510908857
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.9315109088566276, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.93)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
1.30621500767
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: 0.79929039236
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 0.7992903923597043, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.80)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 195
\-------------------------
Environment.reset(): Trial set up with start = (6, 2), destination = (1, 6), deadline = 25
0.476637040083
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4766; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None)
1.81194808831
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: None, reward: 2.21490477853
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.2149047785302987, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.21)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: forward, reward: -9.03754698152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -9.037546981517295, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.04)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: right, reward: 0.114251983359
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.11425198335910602, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.11)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.89387826196
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: None, reward: 2.6299176673
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.6299176672974927, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.63)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
1.08480411749
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: left, reward: 2.69261153024
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 2.69261153023826, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.69)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: -10.1008408864
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -10.100840886403706, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.10)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: -10.8314584957
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': -10.83145849574071, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.83)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: -5.22790180145
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': -5.227901801449506, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.23)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 1.12353489231
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.1235348923053254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.12)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.05275270001
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 1.17831839675
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 1.1783183967539754, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.18)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: -5.36944193743
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 15, 't': 10, 'action': None, 'reward': -5.369441937431518, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.37)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: -39.9853149471
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': -39.98531494706137, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.99)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: -39.1155898896
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -39.11558988961649, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.12)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
1.84972995718
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.42075932856
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.42075932855868, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.42)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: -40.0161116574
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -40.01611165737861, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.02)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, 'forward')
1.67499117166
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.96743654724
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.9674365472433961, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.97)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
2.00311084164
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 1.75698343119
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 1.7569834311927865, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.76)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: -40.9163367487
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 8, 't': 17, 'action': 'left', 'reward': -40.9163367487124, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.92)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: -10.8042425777
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': -10.804242577748099, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.80)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'left', 'right')
1.77114862107
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: 1.43589777105
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.435897771051639, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.44)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: 0.679298803812
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 0.6792988038124994, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.68)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', 'left', 'left')
1.72643382322
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: 1.16448582643
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 4, 't': 21, 'action': None, 'reward': 1.1644858264346867, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.16)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'left', None)
0.960330532398
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: forward, reward: 1.59273309078
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 1.5927330907788946, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.59)
8% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 196
\-------------------------
Environment.reset(): Trial set up with start = (2, 4), destination = (5, 6), deadline = 25
0.474829256295
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4748; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.4876623224
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 1.22110622945
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.2211062294520338, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.22)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, 'left')
1.8937841595
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 2.32850318073
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.328503180733813, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.33)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.35438427592
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 2.26469928334
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.2646992833399917, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.26)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
1.80954177963
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 2.17614062023
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.1761406202268647, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.18)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: right, reward: 0.445627762389
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.4456277623893782, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.45)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, 'forward')
1.89142869823
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: right, reward: 2.7494888501
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 2.749488850095785, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.75)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: left, reward: -10.5742343895
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -10.574234389496379, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.57)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', None)
1.3405569483
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: 1.51919452525
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.5191945252464798, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.52)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'forward', 'left')
1.87887893391
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: forward, reward: 1.50835916177
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.5083591617663987, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.51)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 1.0149852859
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.0149852858984936, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.01)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
1.63844162526
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 1.0834171544
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.0834171544007511, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.08)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
1.88870782387
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: left, reward: 0.882428912962
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 0.8824289129620748, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.88)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
1.34636261862
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: 1.5717209683
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.5717209682977107, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.57)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
1.27653181159
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: 2.11905195954
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 2.1190519595396133, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.12)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'left', 'forward')
2.3162546791
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: 1.87223788448
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 1.8722378844792973, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 1.87)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 197
\-------------------------
Environment.reset(): Trial set up with start = (8, 3), destination = (5, 5), deadline = 25
0.47302832905
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4730; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 0.595864257282
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.5958642572819179, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 0.60)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'left')
1.86055915937
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: right, reward: 2.68168787721
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 2.681687877206694, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.68)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
1.21753548747
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 1.02554606987
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.0255460698671965, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.03)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.45904179346
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.00433609923
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.0043360992278272, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.00)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
1.23168894634
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.03197938771
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.0319793877114924, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.03)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: -9.75970465852
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -9.759704658520747, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -9.76)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: 1.50833694266
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 1.5083369426608018, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 1.51)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: None, reward: -4.21070544516
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 18, 't': 7, 'action': None, 'reward': -4.210705445160507, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.21)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
1.12154077867
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 1.67469883809
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.6746988380935703, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.67)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.14399826147
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.1439982614659865, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.14)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
2.13962145217
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 0.878845297371
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 0.8788452973705632, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.88)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.40379355873
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.4037935587314867, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.40)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: -10.877365479
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -10.877365478992802, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.88)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: -5.58968678129
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': -5.589686781290254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.59)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'forward')
1.53015510143
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 1.85565903758
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 1.8556590375844955, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.86)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'left', 'left')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 0.716702350178
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': 0.7167023501784653, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 0.72)
36% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 198
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (1, 5), deadline = 25
0.471234232342
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4712; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: left, reward: -20.6940237078
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -20.69402370784413, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.69)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'right', None)
0.605369869284
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 0.509853929943
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.5098539299431923, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.51)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'left')
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 2.13935566069
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 2.1393556606911566, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.14)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 0.507042215525
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.5070422155249383, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 0.51)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: -4.96149224085
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': -4.961492240847664, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.96)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: forward, reward: -40.6655597114
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -40.66555971144007, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.67)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'forward', 'right')
1.06273093844
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 1.80167612466
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.8016761246604764, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.80)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 1.30384583433
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.3038458343330546, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent drove right instead of left. (rewarded 1.30)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
1.46509298246
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: 1.94197493102
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 1.941974931017871, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.94)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None)
1.36092938983
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 2.59569926911
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.5956992691052836, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.60)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'left', 'left')
1.77444112851
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: 1.15350730673
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 1.1535073067309833, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 1.15)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.02552065815
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.0255206581452287, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.03)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
1.50923337477
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.29423807716
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.2942380771588935, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.29)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: -39.4894427166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': -39.48944271661983, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.49)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: -10.8860413104
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -10.886041310378982, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.89)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, 'right')
1.76330205622
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 1.6666765996
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.6666765996027866, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.67)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
1.88004713642
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 2.54808401991
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 2.548084019910002, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.55)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', 'left')
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: 1.47714513832
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 1.4771451383231597, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.48)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', 'right')
2.09885712709
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 2.24116300693
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 7, 't': 18, 'action': None, 'reward': 2.24116300692895, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.24)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', 'right')
2.17001006701
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 1.97189472996
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.971894729962637, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.97)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None)
1.13183416703
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 1.4200961452
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.4200961451991851, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.42)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
1.69779188556
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: 2.23064505922
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 2.2306450592177995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.23)
12% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 199
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (3, 4), deadline = 20
0.469446940265
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4694; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', None)
1.57206915311
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 1.32149965987
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.3214996598717503, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.32)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: -9.1758502619
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -9.175850261898463, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.18)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: -10.8371962549
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.8371962548975, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.84)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
1.8894803265
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 0.984461802203
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 0.9844618022033922, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.98)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: -4.53764592201
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': -4.537645922012466, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.54)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 1.03192906782
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.0319290678237552, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.03)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'forward', None)
1.44678440649
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 2.49728176566
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 2.49728176565561, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.50)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: -4.37855019853
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': None, 'reward': -4.378550198525282, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.38)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'forward')
2.32045877416
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 1.39688132851
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.3968813285142392, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.40)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
1.99284119993
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.10013598687
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.1001359868722975, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.10)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'forward', None)
1.30492426497
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 0.104387300593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 0.10438730059344914, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.10)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'forward')
2.09323582151
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: 2.04738282989
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 2.0473828298922374, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.05)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -9.94304295758
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -9.94304295757949, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.94)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'forward', 'right')
1.43220353155
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 1.05973044304
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.059730443041559, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.06)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'forward', 'forward')
1.4062677703
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 2.41063225582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.410632255820196, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.41)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: -40.3756041143
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -40.37560411432219, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.38)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: -5.46321460463
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 4, 't': 16, 'action': None, 'reward': -5.463214604633175, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.46)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
1.11553554838
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: left, reward: 2.24369645897
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 2.2436964589721455, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.24)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', None)
1.43697106435
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: None, reward: 1.05955855812
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.0595585581164304, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.06)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'forward', 'left')
0.861728786116
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: -0.585308951885
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': -0.5853089518849824, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded -0.59)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 200
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (3, 2), deadline = 25
0.46766642701
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4677; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: left, reward: -9.90201367771
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -9.902013677709519, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.90)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: left, reward: -10.0130846401
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -10.013084640051016, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.01)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
1.42369686818
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 1.82559090284
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.8255909028383694, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.83)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: -4.59333146212
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 22, 't': 3, 'action': None, 'reward': -4.593331462119397, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.59)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 1.33249676443
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.3324967644290537, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded 1.33)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: -9.48605501643
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -9.486055016427034, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.49)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
1.38556836841
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: 2.47147796457
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 2.4714779645665326, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.47)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
1.54961853424
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.29659523854
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.2965952385437185, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.30)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -10.2437684117
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -10.243768411650805, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.24)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', None)
1.92310688639
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 1.58763647146
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.587636471457329, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.59)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
1.75537167892
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 1.94607119525
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.946071195253235, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.95)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: -19.5447551254
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -19.54475512542438, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.54)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: -5.11200555906
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': -5.112005559055666, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.11)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 0.456471007022
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 0.4564710070215041, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.46)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 1.00418510366
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.0041851036587333, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.00)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: -10.2350973447
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': -10.235097344684426, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.24)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
1.6312054015
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 2.19863308223
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 2.198633082225975, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.20)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, 'left')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: -0.221659038983
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 8, 't': 17, 'action': 'left', 'reward': -0.2216590389825086, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded -0.22)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
1.91491924186
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 1.09469300482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.0946930048220431, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.09)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: left, reward: -9.61174718808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': -9.61174718808354, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.61)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'right', 'right')
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: -10.5720541662
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', 'right'), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': -10.572054166190865, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'right')
Agent attempted driving forward through a red light. (rewarded -10.57)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: -4.93370579259
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 4, 't': 21, 'action': None, 'reward': -4.933705792589585, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.93)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, 'right')
1.84979288988
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 1.8063298433
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 1.8063298433041908, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.81)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'forward', None)
1.85072143709
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.68507429382
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 2, 't': 23, 'action': None, 'reward': 1.6850742938159355, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.69)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'right', None)
1.98607360135
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 0.438133658602
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 1, 't': 24, 'action': None, 'reward': 0.43813365860249487, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.44)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 201
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (2, 5), deadline = 20
0.465892666866
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4659; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: -4.28785148175
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': -4.28785148174885, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.29)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'forward')
2.0703093257
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 2.3914681567
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 2.3914681567017677, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.39)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'left')
0.810295427491
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.57860003243
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.578600032432252, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.58)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: right, reward: 1.63635427764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.6363542776383375, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent drove right instead of forward. (rewarded 1.64)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'forward')
2.25383684515
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: None, reward: 1.45325724538
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.4532572453802004, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.45)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: -39.7012051591
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': -39.70120515914585, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.70)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: -10.9220535607
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -10.92205356074524, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.92)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: None, reward: -4.57377386802
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': -4.5737738680207904, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.57)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'left', None)
1.92852316649
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: left, reward: 1.66488456808
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 1.6648845680846776, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.66)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.90173572596
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: 1.09838560776
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.098385607758181, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.10)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: 1.75061957232
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.7506195723199456, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.75)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'right', None)
1.38730830986
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: forward, reward: 1.27800285599
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.278002855990702, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.28)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 202
\-------------------------
Environment.reset(): Trial set up with start = (5, 3), destination = (1, 5), deadline = 30
0.46412563422
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4641; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: forward, reward: -10.7661561824
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': -10.76615618236061, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.77)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 2.59814540104
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.5981454010426273, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.60)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: -39.2609769185
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 28, 't': 2, 'action': 'left', 'reward': -39.26097691846686, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.26)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
1.61824073635
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 2.30273105901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.302731059006889, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.30)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: -10.3345685988
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': -10.334568598781722, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.33)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: forward, reward: -9.01467048751
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': -9.014670487510045, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.01)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'forward')
1.85867005134
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: right, reward: 2.11803577119
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 2.1180357711949447, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.12)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: 1.71207356264
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.712073562638188, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.71)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
2.21406557816
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 1.97499704532
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': 1.9749970453157362, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.97)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: -5.60527302804
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 21, 't': 9, 'action': None, 'reward': -5.605273028039065, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.61)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 0.942909733349
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 0.9429097333494658, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.94)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, 'left')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 1.37331791562
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.3733179156191935, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded 1.37)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 1.21066084209
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 1.2106608420901692, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.21)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'right', None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 2.25900915306
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 2.2590091530625904, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 2.26)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', None)
1.79670386729
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: 1.360694677
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 16, 't': 14, 'action': 'left', 'reward': 1.3606946770001014, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.36)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 203
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (4, 4), deadline = 20
0.462365303557
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4624; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', None)
2.13305584888
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.21071262294
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.2107126229428933, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.21)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.5464885934
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 2.00924229091
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.009242290909838, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.01)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'right', None)
1.67188423591
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 2.32121269817
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.321212698171715, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.32)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
1.77786544216
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: None, reward: 1.39987711456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.3998771145610558, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.40)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: 1.32778104336
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.3277810433563983, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.33)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
1.97831432947
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 1.40582828393
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.4058282839260894, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.41)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
1.57869927214
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: left, reward: 1.49692774775
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 1.4969277477463763, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.50)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.50006066686
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 0.891426841153
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 0.8914268411527746, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.89)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 2.64648138217
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.6464813821716753, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.65)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
2.09453131174
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 1.97617853271
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.976178532709861, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.98)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'forward')
1.69290706951
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: forward, reward: 2.05811379558
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 2.0581137955776088, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.06)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
1.58887127836
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: None, reward: 1.74776956481
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.7477695648098595, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.75)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'right', 'forward')
0.982186069298
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: None, reward: 0.957231482648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.957231482648317, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 0.96)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: right, reward: 1.2903474406
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.2903474406038282, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.29)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'left')
2.20523958949
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 1.47232856915
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.4723285691534163, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.47)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: -9.34368738819
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -9.343687388188304, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.34)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: -10.4459161684
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -10.445916168431332, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.45)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: -20.0885611775
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -20.088561177516823, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.09)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'left')
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 1.12270373995
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 1.1227037399487123, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded 1.12)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 1.99587989185
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 1.9958798918536298, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.00)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 204
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (7, 2), deadline = 20
0.460611649456
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4606; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'right')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 1.9702862038
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.9702862038000222, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent followed the waypoint forward. (rewarded 1.97)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
1.27596515611
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.87502310111
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.8750231011145515, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.88)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'right')
2.07095239849
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.04144887056
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.0414488705558487, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.04)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.27858020543
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.2785802054305897, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.28)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
2.2308887412
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: 1.99530044295
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 1.9953004429536432, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.00)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: -5.07429924563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': -5.0742992456319245, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.07)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: -5.26025488555
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': -5.260254885550614, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.26)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
2.03535492222
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 2.10395351455
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.1039535145482526, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.10)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: -9.16342642173
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.163426421732208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.16)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', 'right')
2.05620063452
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.59579599556
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.5957959955554895, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.60)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
1.39052787543
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 1.10848841976
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.1084884197552662, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.11)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: -40.0300183646
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -40.03001836457762, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.03)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
1.67232961915
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.69657048059
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.6965704805879191, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.70)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
0.704655782784
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 0.822190201514
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.8221902015144057, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.82)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: left, reward: -39.6812343102
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -39.68123431022297, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.68)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', None)
1.53781350995
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: 0.720029441868
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 0.7200294418682043, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.72)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: -4.79829000994
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 16, 'action': None, 'reward': -4.7982900099377765, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.80)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, None)
1.66832042158
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 1.14906609851
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.1490660985072898, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.15)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
1.40869326005
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 0.948326867779
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.948326867778615, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.95)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', None)
1.6920713067
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 2.04118895501
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 2.0411889550066196, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.04)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 205
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (1, 7), deadline = 20
0.458864646596
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4589; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', 'left')
0.138209917116
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: 1.13893950065
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.1389395006485719, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded 1.14)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'left')
1.83878407932
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: right, reward: 2.04318483935
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.0431848393525582, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.04)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: -9.59840237379
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -9.598402373790195, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.60)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.76789786545
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 1.51703671705
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.5170367170548187, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.52)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 1.79690921868
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.796909218677273, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.80)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: right, reward: 0.1398355646
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.13983556460020252, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 0.14)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'right', 'forward')
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: left, reward: -40.5914789529
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -40.591478952929116, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.59)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.17851006391
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 1.26491024394
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.2649102439394913, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.26)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: forward, reward: -39.9808078643
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -39.98080786425215, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.98)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
1.68445004987
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 1.74565882746
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.7456588274554832, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.75)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
1.71505443866
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 1.51578084395
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.515780843951583, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.52)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
0.763422992149
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: forward, reward: 0.434454738394
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 0.4344547383937891, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.43)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: forward, reward: 0.754956356108
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.754956356108341, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.75)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: -40.7627327481
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -40.76273274809919, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.76)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', 'left')
1.86473120898
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 1.38723241564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.387232415643116, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.39)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', 'left')
1.62598181231
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 1.77210307461
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.7721030746138557, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.77)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: 0.510800055074
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.5108000550743292, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.51)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'forward')
1.85354704526
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 1.27844414892
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.2784441489206342, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.28)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: -9.63875237227
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': -9.638752372273402, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.64)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'right', None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: -5.45924701667
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 1, 't': 19, 'action': None, 'reward': -5.459247016669916, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.46)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 206
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (8, 2), deadline = 20
0.457124269749
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4571; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'forward')
1.98835291127
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 1.10311863031
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.103118630309497, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.10)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: -4.82097327839
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 19, 't': 1, 'action': None, 'reward': -4.820973278385998, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.82)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'forward', 'right')
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: -4.78432317736
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 18, 't': 2, 'action': None, 'reward': -4.784323177361524, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.78)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: forward, reward: 2.35299000183
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 2.3529900018280365, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.35)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None)
1.42987573678
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 1.92407706472
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.9240770647216792, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.92)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
1.39811980838
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.85484223571
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.8548422357133205, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.85)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 207
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (8, 3), deadline = 25
0.455390493785
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4554; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'right')
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: -39.0835430358
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': -39.08354303582978, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.08)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
2.07549412861
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 2.69287482582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.6928748258246866, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.69)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'right')
2.32599831504
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 2.56828529175
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.568285291753167, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.57)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: right, reward: 0.961110133317
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.9611101333167307, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.96)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'right')
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: right, reward: 0.74524579498
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.7452457949799917, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent drove right instead of left. (rewarded 0.75)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', 'forward')
1.8736997893
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.57124757072
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.5712475707223383, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.57)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'left', None)
1.86663013085
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.30988747845
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.309887478453436, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.31)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: left, reward: 1.00915637813
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 1.0091563781314214, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.01)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.22171015393
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 1.82781372546
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.827813725461361, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.83)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.67961600368
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: 2.85259961582
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 2.852599615815448, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.85)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'left')
2.12780193763
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: None, reward: 1.54551962746
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.5455196274565948, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.55)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: -40.2992794157
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -40.29927941566969, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.30)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: right, reward: -0.0999903290631
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': -0.09999032906309813, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.10)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: None, reward: -4.29418505813
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': -4.294185058129263, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.29)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: forward, reward: 0.688744757177
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 0.6887447571765012, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.69)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: forward, reward: -9.45959797032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': -9.459597970317533, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.46)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
1.52476193969
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: None, reward: 2.16517913935
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': 2.1651791393520465, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.17)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: right, reward: 1.4003190943
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 1.4003190943022739, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.40)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'forward')
1.54573577079
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: right, reward: 2.36140931145
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 2.3614093114465184, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.36)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: forward, reward: -0.137644250266
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': -0.1376442502658789, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded -0.14)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', 'forward', 'left')
2.15437832434
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: 0.751845967787
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 5, 't': 20, 'action': None, 'reward': 0.7518459677866396, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 0.75)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', 'left', None)
2.03595413537
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: right, reward: 1.27954827072
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 1.279548270717929, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.28)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'left', None)
1.24950814759
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: forward, reward: 1.16026781191
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 1.1602678119106569, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.16)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: -5.04962594406
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 2, 't': 23, 'action': None, 'reward': -5.049625944061765, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.05)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, 'left')
2.07965460988
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 0.802464639817
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': 0.8024646398172353, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 0.80)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 208
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (2, 6), deadline = 20
0.453663293667
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4537; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 1.0979241696
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.0979241696046946, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.10)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
1.67680668729
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 1.54033613984
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.5403361398368236, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.54)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 1.082416523
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.0824165229961047, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.08)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'right', 'forward')
1.98534491292
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 1.61069596703
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.6106959670281613, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 1.61)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
2.11309459208
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: 1.19323082672
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 1.1932308267225447, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.19)
75% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 209
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (7, 3), deadline = 30
0.451942644455
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
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epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4519; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'forward')
1.90845001306
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: None, reward: 2.81605202004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 30, 't': 0, 'action': None, 'reward': 2.816052020035256, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.82)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: right, reward: 0.457485412724
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 0.45748541272440735, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.46)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: forward, reward: -10.4308742533
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': -10.430874253290458, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.43)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: None, reward: 2.24947068751
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.2494706875112285, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.25)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: left, reward: -10.4429011572
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': -10.442901157244204, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.44)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', 'forward')
2.09424628179
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: forward, reward: 1.75353264064
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 1.7535326406404692, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 1.75)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
1.83666078255
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.26952989068
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 24, 't': 6, 'action': None, 'reward': 1.269529890680033, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.27)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 0.552697758253
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 0.5526977582526188, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.55)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: forward, reward: -10.9848158245
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': -10.9848158245027, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.98)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 0.0643807178724
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 0.06438071787238453, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove right instead of left. (rewarded 0.06)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
1.84497053952
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.69370775708
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.6937077570779109, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.69)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
2.26610780975
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: 2.11754948909
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': 2.1175494890897744, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.12)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'left')
1.70353395674
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: 1.13626759736
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 18, 't': 12, 'action': 'left', 'reward': 1.1362675973625131, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.14)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'left', None)
2.31682758237
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 1.59324118229
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 13, 'action': None, 'reward': 1.5932411822943553, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.59)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', 'right')
2.4471418034
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 1.12991200715
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.1299120071457494, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.13)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'left', 'right')
1.42436703783
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 1.95454476563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 1.954544765631897, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 1.95)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: forward, reward: 1.49046744043
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 1.4904674404282858, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.49)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'right', None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: 2.07820035156
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 2.07820035155659, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 2.08)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'left')
1.26173807392
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: forward, reward: 0.810022342583
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': 0.8100223425828503, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 0.81)
37% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 210
\-------------------------
Environment.reset(): Trial set up with start = (3, 4), destination = (5, 2), deadline = 20
0.450228521303
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4502; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', None)
1.84893216069
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 2.87129080569
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.8712908056868445, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.87)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: right, reward: 1.41965996161
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.4196599616084462, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.42)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: forward, reward: 0.930393788831
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 0.9303937888305688, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 0.93)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
2.36011148319
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 2.76850316708
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.768503167083479, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.77)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: 1.04309646084
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.0430964608412074, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.04)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: -10.1640959045
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -10.164095904506324, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -10.16)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'right', 'left')
0.932499091459
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: 1.01395280588
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.0139528058765577, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent properly idled at a red light. (rewarded 1.01)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: left, reward: 0.357552470553
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 0.35755247055301553, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.36)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'right', None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 2.72033105189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.720331051893046, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.72)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'right')
1.82806136659
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 1.36133613405
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.36133613405315, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.36)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'right', 'forward')
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: -20.8516319347
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -20.851631934723326, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.85)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'forward')
1.9416272113
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 2.35197595983
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.3519759598306225, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.35)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
1.60857141356
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: right, reward: 2.60404973377
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 2.6040497337703172, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.60)
35% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 211
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (7, 4), deadline = 25
0.448520899458
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4485; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'right', 'forward')
1.54245382715
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: None, reward: 2.73134347843
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.7313434784341784, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 2.73)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'right', None)
2.08145965994
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: 1.53260892308
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.5326089230766204, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.53)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
1.62464388551
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: right, reward: 1.04792160944
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.0479216094353452, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.05)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
1.85089477527
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 1.27155426749
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 1.271554267493837, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.27)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', 'left')
1.44545982483
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 2.43986194353
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.43986194352676, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.44)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', 'right')
1.68945590173
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 2.91568789258
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.9156878925766763, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 2.92)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.91049790597
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.9104979059742233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.91)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.47567941183
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.4756794118270176, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.48)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'forward')
1.3230683047
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.61588847736
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.615888477360729, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.62)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.71535689708
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.89337444874
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.8933744487384707, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.89)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
1.80436567291
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.59221601435
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.5922160143491393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.59)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
1.20488797975
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 1.54275067324
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 1.5427506732445104, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.54)
52% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 212
\-------------------------
Environment.reset(): Trial set up with start = (8, 5), destination = (4, 7), deadline = 30
0.446819754263
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4468; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: forward, reward: 0.86213617067
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': 0.8621361706701434, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.86)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'left', 'left')
1.46397421762
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: left, reward: 2.05877483115
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 29, 't': 1, 'action': 'left', 'reward': 2.058774831151717, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 2.06)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.36500516528
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.365005165278183, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.37)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: -9.2754774989
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': -9.2754774988983, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.28)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.15681963805
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.1568196380455666, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.16)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
1.34958317408
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.76581920983
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.7658192098305936, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.77)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: -4.32627513261
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 24, 't': 6, 'action': None, 'reward': -4.3262751326100926, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.33)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.56122452138
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 2.23447720154
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 2.2344772015434273, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.23)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
1.89785086146
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 2.82234837832
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': 2.822348378319372, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.82)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 0.346815467347
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 0.34681546734720303, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.35)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward')
1.56599559709
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 1.90832457636
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.9083245763593388, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.91)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'forward', None)
1.61541764131
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 1.0080744958
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 11, 'action': None, 'reward': 1.0080744957994048, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.01)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: -10.8952717696
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': -10.895271769598434, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.90)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: -4.74096091902
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 17, 't': 13, 'action': None, 'reward': -4.740960919020962, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.74)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'forward', 'left')
1.18449050711
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: forward, reward: 0.470630275246
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': 0.47063027524575696, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 0.47)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'left')
1.41990077705
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: left, reward: 2.50919041265
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 15, 't': 15, 'action': 'left', 'reward': 2.5091904126464675, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.51)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, 'right')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: left, reward: 1.2602731218
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 1.2602731218028116, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.26)
43% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 213
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (2, 6), deadline = 30
0.445125061153
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4451; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
2.19182864942
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: left, reward: 1.15401092091
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 1.1540109209062441, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.15)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
1.87551043254
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 2.04022698889
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': 2.0402269888870563, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.04)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.19574375401
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.86566746284
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.8656674628383096, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.87)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: -39.4188862224
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 27, 't': 3, 'action': 'left', 'reward': -39.41888622237312, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.42)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: -10.4362397054
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': -10.436239705389973, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.44)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: -9.61011370622
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 5, 'action': 'left', 'reward': -9.610113706224936, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.61)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: right, reward: 0.695331092299
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 0.6953310922991068, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 0.70)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.7693391483
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: None, reward: 1.84297728194
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.8429772819360333, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.84)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: -10.5655090505
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': -10.565509050507062, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.57)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
1.80615821512
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: None, reward: 1.18983688222
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': 1.1898368822220582, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.19)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 1.47230200952
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 1.4723020095177235, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.47)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
1.49799754867
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.12139439274
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 11, 'action': None, 'reward': 1.1213943927425676, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.12)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.35555645316
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.355556453163815, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.36)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'forward')
1.6531627094
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 2.16446104463
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 17, 't': 13, 'action': 'left', 'reward': 2.1644610446336774, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.16)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'forward')
1.90881187702
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: left, reward: 1.35926450675
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 16, 't': 14, 'action': 'left', 'reward': 1.3592645067542444, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.36)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: 2.41452407738
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 2.4145240773845216, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.41)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'forward')
2.12949254563
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.3248510886
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.3248510886029317, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.32)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, 'left')
1.55309533661
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.13878289501
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 13, 't': 17, 'action': None, 'reward': 1.1387828950137722, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.14)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', 'left')
1.69444772996
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 2.24880731799
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 12, 't': 18, 'action': None, 'reward': 2.248807317986436, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.25)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'forward', None)
1.55770119196
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 2.18717292763
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.18717292762736, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.19)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: -19.0540592809
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 10, 't': 20, 'action': 'right', 'reward': -19.05405928093813, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.05)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'forward', None)
1.67697640075
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 2.52836414214
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 2.5283641421444263, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.53)
27% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 214
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (4, 6), deadline = 25
0.443436795657
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4434; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'right', None)
1.55345786426
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.6742171295
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.6742171294978088, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 1.67)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.50445309579
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.5044530957944495, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.50)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
2.10631057367
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 2.19914838585
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 2.1991483858468133, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.20)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
1.72520214282
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 2.55188936763
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 2.551889367630655, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.55)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
2.36009961989
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 1.18092485008
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.1809248500800213, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.18)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.3738193265
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: forward, reward: 2.33959642497
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.339596424973175, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.34)
76% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 215
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (3, 5), deadline = 25
0.441754933395
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4418; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: -5.25999195404
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': -5.259991954043582, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.26)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'forward', None)
0.598938865271
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: 0.572033964979
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 0.5720339649785187, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.57)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: 0.101398565787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 0.10139856578659168, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.10)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: -4.22726610439
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 22, 't': 3, 'action': None, 'reward': -4.227266104388487, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.23)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
1.63403819189
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: left, reward: 1.63095375183
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.6309537518310195, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.63)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: left, reward: -39.2648147077
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -39.26481470769913, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.26)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
1.53070560842
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.79258489188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.792584891875512, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.79)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
1.72717181712
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.63934293493
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.639342934933809, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.64)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
2.16164525015
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.75617195102
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.756171951020886, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.76)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: 1.27071962544
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.2707196254394975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.27)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: forward, reward: 0.0774887171052
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 0.07748871710515381, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.08)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: right, reward: 2.2043902592
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 2.204390259200095, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.20)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: None, reward: 2.14866709665
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.14866709665253, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.15)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: forward, reward: 1.15741926729
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.1574192672922332, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.16)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'forward', 'forward')
1.37045607789
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: right, reward: 0.887876842691
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.8878768426908263, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 0.89)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: None, reward: 2.16245169429
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 10, 't': 15, 'action': None, 'reward': 2.162451694289576, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.16)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'left', None)
2.56430732513
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: None, reward: 2.33103941569
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 2.331039415691925, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.33)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'left', None)
1.35042383194
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: 0.671771074063
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 0.6717710740625416, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 0.67)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 216
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (3, 4), deadline = 20
0.440079450083
Simulating trial. . .
epsilon = 0.4401; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4401; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4401; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4401; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4401; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4401; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4401; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4401; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4401; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: -39.4399459485
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -39.439945948473394, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.44)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 0.841511290689
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.841511290689353, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.84)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, 'left')
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: -10.956478262
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.95647826197909, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.96)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, 'left')
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 0.369061029141
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 0.36906102914112016, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded 0.37)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: None, reward: -5.87581378308
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': -5.875813783079926, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.88)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 2.03340980999
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.033409809985466, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.03)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'right', 'left')
0.24016411661
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: right, reward: 2.18077311304
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 2.18077311304077, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left')
Agent followed the waypoint right. (rewarded 2.18)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 0.123320521451
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 0.12332052145051442, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent drove right instead of forward. (rewarded 0.12)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: -9.19314678005
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.193146780047353, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.19)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
1.06903892702
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: left, reward: 1.15966899104
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.159668991039475, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.16)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.77051223499
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: forward, reward: 1.2314590518
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.2314590517983588, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.23)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: -4.31937494448
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': None, 'reward': -4.3193749444813205, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.32)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: left, reward: 0.994914638644
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 0.9949146386438537, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.99)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.50098564339
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: 1.44102336691
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.441023366914908, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.44)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 1.76490999788
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.7649099978752332, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.76)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: -39.3519581034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -39.35195810344823, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.35)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: -5.63293576117
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 4, 't': 16, 'action': None, 'reward': -5.6329357611719715, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.63)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'right')
1.87246406411
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: 0.37185601143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 0.3718560114295395, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 0.37)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 217
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (3, 2), deadline = 25
0.438410321525
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4384; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'right')
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: -9.41111094111
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -9.411110941106903, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -9.41)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
2.09306964487
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.04646347334
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.0464634733449656, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.05)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'right', None)
2.06444552389
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.50062770678
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.500627706778477, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.50)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
2.08274808069
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.94285046039
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.9428504603888221, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.94)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
2.01279927054
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.3039923349
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.3039923348994877, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.30)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: forward, reward: 1.89149408927
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.8914940892704837, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.89)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'right')
1.59469875032
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: right, reward: 1.30648425749
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.3064842574923796, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.31)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
2.18325737603
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 1.73075780765
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.7307578076539016, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.73)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
2.11190929923
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 1.82232172332
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.8223217233220546, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.82)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'right')
1.12216003777
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: forward, reward: 1.87305632153
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.8730563215345029, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 1.87)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
1.33262621194
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 2.46549966761
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.4654996676081447, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.47)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
1.89906293977
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 2.61785460117
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.6178546011691104, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.62)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: left, reward: -9.56684611175
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -9.566846111748303, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.57)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'right', None)
0.557611899613
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: right, reward: -0.0194733618482
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': -0.0194733618482128, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded -0.02)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: forward, reward: 0.228117707994
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 0.22811770799366393, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.23)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'forward')
1.95357254112
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: right, reward: 1.73242446828
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 1.7324244682782706, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.73)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, 'left')
1.68050342606
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: None, reward: 0.823741860897
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 9, 't': 16, 'action': None, 'reward': 0.8237418608973563, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.82)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: None, reward: -5.42667485836
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 17, 'action': None, 'reward': -5.426674858362913, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.43)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, None)
1.62648102205
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: right, reward: 0.568470124153
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 0.5684701241532422, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.57)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, 'left')
1.36847458155
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: right, reward: 1.96737927292
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.9673792729153665, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.97)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', None, 'right')
1.78199385612
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: 1.58071160922
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 5, 't': 20, 'action': 'left', 'reward': 1.5807116092151192, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.58)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', 'left', 'forward')
1.46947839103
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 2.16871155393
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 4, 't': 21, 'action': None, 'reward': 2.168711553929233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.17)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'left', None)
1.56371375059
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: forward, reward: 0.367945151641
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.36794515164083563, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.37)
8% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 218
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (3, 4), deadline = 20
0.436747523621
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4367; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: left, reward: 1.52683629048
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.526836290484631, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.53)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 1.41764067222
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.4176406722158834, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.42)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: forward, reward: -10.0579664539
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.05796645391896, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.06)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
1.83804972134
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 2.04311124831
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.043111248307924, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.04)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'right', None)
0.269069268883
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: right, reward: 0.638068649989
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.6380686499890914, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.64)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: left, reward: -40.5883089037
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': -40.5883089036719, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.59)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
1.96711551128
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 2.17193949866
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.171939498664999, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.17)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
1.87243705979
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 1.72289928838
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.7228992883820453, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.72)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: forward, reward: -10.8436164995
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -10.843616499484039, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.84)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: right, reward: 1.44059258707
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.440592587069267, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 1.44)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'left')
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: 0.551904767398
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.5519047673977416, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded 0.55)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'left')
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 2.02809642775
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.028096427749008, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.03)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: right, reward: 1.88087308869
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.8808730886935772, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.88)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: right, reward: -0.185572799764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': -0.18557279976434893, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded -0.19)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: left, reward: -10.8459941483
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -10.845994148274274, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.85)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
2.06952750497
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 1.34677435836
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.3467743583565115, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.35)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
1.70815093166
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 0.776639583865
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 4, 't': 16, 'action': None, 'reward': 0.7766395838652109, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.78)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', None)
2.10267027145
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: forward, reward: 2.03827888606
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 2.038278886060307, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.04)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, 'forward')
1.95700759184
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: None, reward: 0.537876997782
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.5378769977820173, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.54)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
1.24239525776
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: None, reward: 0.962702463405
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.9627024634046661, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.96)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 219
\-------------------------
Environment.reset(): Trial set up with start = (8, 5), destination = (4, 4), deadline = 25
0.435091032357
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4351; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
2.15839580272
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.80028227648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.8002822764826023, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.80)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
1.9793390396
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 2.79776032591
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.7977603259094126, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.80)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'right', None)
2.28253661533
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.97468481654
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.974684816543696, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.97)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
2.38854968275
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 2.64242346832
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.6424234683198833, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.64)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
1.0974755731
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 1.62776380462
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.6277638046181513, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.63)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: left, reward: 1.27382790349
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 1.273827903487331, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 1.27)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: 1.22565346263
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.225653462628105, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.23)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'right', 'left')
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: left, reward: -20.5583335618
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -20.558333561793624, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.56)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: 1.80367778518
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.803677785181446, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 1.80)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 1.6755038004
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.675503800401907, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.68)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: -10.4757392253
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': -10.475739225294392, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.48)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
2.07047457875
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: 2.60614374912
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 2.606143749119433, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.61)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.47100450515
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: 1.52621828798
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 1.5262182879822228, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.53)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, 'right')
1.49760817965
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: 2.50442930065
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 2.504429300651799, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 2.50)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
1.10254886058
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 1.95514627467
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.9551462746669346, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.96)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: left, reward: -9.03290592306
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -9.03290592306243, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.03)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', 'forward')
1.76130743287
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 2.20754675303
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 2.2075467530250235, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 2.21)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: -4.89074524186
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 17, 'action': None, 'reward': -4.890745241859959, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.89)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: 0.119400007085
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 18, 'action': 'forward', 'reward': 0.11940000708533227, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.12)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'left', None)
1.011097453
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: right, reward: 1.33599235808
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.3359923580766428, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.34)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: right, reward: 1.9849314123
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 1.9849314123013913, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.98)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'left', 'right')
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: forward, reward: 0.0276781692749
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.027678169274903408, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 0.03)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'red', None, None)
1.94058048483
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 0.401670796651
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 3, 't': 22, 'action': None, 'reward': 0.4016707966508386, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.40)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', None, 'right')
1.70165087644
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 0.708291461328
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.7082914613278533, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 0.71)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
1.17112564074
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 2.00959372565
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 24, 'action': None, 'reward': 2.0095937256499776, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.01)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 220
\-------------------------
Environment.reset(): Trial set up with start = (5, 7), destination = (7, 4), deadline = 25
0.433440823817
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4334; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
1.63249597186
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: left, reward: 1.67456952635
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.6745695263460272, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.67)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'right')
1.78852690527
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: None, reward: 2.85845603378
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.85845603378282, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.86)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'left')
1.92657939508
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: None, reward: 2.74955009684
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.749550096839407, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.75)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
1.17818324339
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: left, reward: 1.66722673102
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': 1.6672267310151323, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 1.67)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', None)
1.33628274747
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: right, reward: 2.6226460901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 2.6226460901022746, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.62)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.59035968319
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 2.45141021224
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.4514102122394164, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.45)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left')
2.11114367011
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 1.33502610851
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.3350261085091466, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.34)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', None)
1.32059512476
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: left, reward: 2.68766069641
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 2.6876606964068737, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.69)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'right', 'right')
0.266309886349
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: right, reward: 1.80767529375
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'right'), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.8076752937521565, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'right')
Agent drove right instead of forward. (rewarded 1.81)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: -9.41686997677
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -9.416869976773466, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -9.42)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: 1.61272372456
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.6127237245556314, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.61)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'left')
1.96454559485
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: left, reward: 0.939385956984
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 0.9393859569843277, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 0.94)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', 'left')
1.69904244346
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: None, reward: 1.65872493937
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.6587249393730694, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.66)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
2.00412791058
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: 1.845770546
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 1.8457705460026865, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.85)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', None)
1.79766817409
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 0.73103076711
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 11, 't': 14, 'action': None, 'reward': 0.7310307671103675, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.73)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: -9.95896926738
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -9.958969267379727, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.96)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: -0.273521157818
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': -0.2735211578183617, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded -0.27)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
1.09910506984
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: forward, reward: -0.0138867174454
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': -0.01388671744538894, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded -0.01)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
1.35552525798
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 0.325055262253
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 0.3250552622533658, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.33)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
2.51548657554
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.38607223411
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 2.38607223410653, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.39)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', None, None)
2.45077940482
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 1.15494525569
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.1549452556868782, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.15)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', None, None)
1.36261968886
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 0.854644975935
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': 0.8546449759354087, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.85)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, None)
1.1086323324
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 1.04719738643
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 1.0471973864330288, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.05)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', 'left', None)
1.4227049872
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: left, reward: -0.509903760685
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -0.5099037606853924, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded -0.51)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', 'right', 'right')
0.532949596171
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: forward, reward: 0.868341955366
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'right'), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': 0.8683419553664811, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'right', 'right')
Agent drove forward instead of right. (rewarded 0.87)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 221
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (7, 2), deadline = 20
0.431796874169
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4318; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'left')
1.59458176359
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.10129640139
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.1012964013915816, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.10)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', 'left')
1.34793908249
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.86461588103
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.8646158810303812, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.86)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', 'forward')
2.36225101655
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.66955563994
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.6695556399386176, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.67)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
1.31174606855
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.32705420755
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.3270542075455252, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.33)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: left, reward: -9.91291644098
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -9.912916440977837, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.91)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
1.31940013805
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.63096900167
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.630969001665697, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.63)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 0.464526104778
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 0.4645261047775635, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 0.46)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.3339172119
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: 1.67860207937
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.6786020793715237, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.68)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
1.50625964564
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: 2.63800400989
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 2.6380040098927595, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.64)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.52884756763
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.4976101919
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.497610191899737, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.50)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: -9.90639739545
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -9.906397395447025, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.91)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'right', None)
1.21210362998
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.85315745299
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.8531574529872386, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.85)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.5831951013
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.5831951012976895, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.58)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: -10.0467958012
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 7, 't': 13, 'action': 'left', 'reward': -10.046795801215074, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -10.05)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: -39.2124892518
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -39.21248925175604, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.21)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 0.253205991624
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.2532059916235463, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent drove right instead of left. (rewarded 0.25)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
1.07791485942
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 0.616251647994
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 0.6162516479944156, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.62)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'right', 'forward')
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: left, reward: -39.7355384774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -39.73553847738328, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.74)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
1.80286233025
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 0.437956615255
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.437956615255348, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.44)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
0.929661205066
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: left, reward: -0.523183507066
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -0.5231835070664607, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.52)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 222
\-------------------------
Environment.reset(): Trial set up with start = (2, 4), destination = (4, 7), deadline = 25
0.430159159675
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4302; alpha = 0.5000
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epsilon = 0.4302; alpha = 0.5000
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/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.67372106216
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.6737210621647385, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.67)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.8601284791
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.860128479101613, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.86)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.02635767249
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.026357672490108, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.03)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.64535357154
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.24310956123
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.2431095612344403, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.24)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: 1.68120201029
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.6812020102880134, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.68)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: -19.2220188457
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': -19.22201884573579, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.22)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'forward', 'left')
1.60627748176
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 1.02344501693
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.0234450169307652, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.02)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', 'left')
1.31486124935
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 1.42865402084
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.4286540208352423, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.43)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', 'left')
1.37175763509
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 1.10879928169
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.108799281690334, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.11)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: forward, reward: 0.258686236458
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 0.25868623645805666, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.26)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: 0.849049088092
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 0.8490490880922874, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.85)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: 0.138857677157
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 0.13885767715664088, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.14)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 2.72349633608
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.7234963360758497, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.72)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: -10.5703944844
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': -10.570394484394924, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.57)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
2.07923167699
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 1.43253611308
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.4325361130792162, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.43)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'left')
1.72308488931
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 2.09688406085
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 10, 't': 15, 'action': None, 'reward': 2.0968840608486445, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.10)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
2.07213182777
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: 1.97985028265
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 1.9798502826534448, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.98)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'right', 'left')
0.585876801219
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 0.936119307248
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 0.9361193072480951, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove right instead of left. (rewarded 0.94)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'right', None)
2.12861071594
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 1.95892032668
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.958920326678651, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.96)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: 1.26137157543
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': 1.2613715754265535, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.26)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
1.75588389503
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: None, reward: 1.25253999637
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.2525399963666397, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.25)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None)
2.02599105521
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: 1.01130274641
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 1.0113027464054005, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.01)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', 'left', 'left')
2.33806474596
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 0.626020638157
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 3, 't': 22, 'action': None, 'reward': 0.6260206381573807, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 0.63)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'left', None)
2.19829084363
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 0.419265441715
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.41926544171529145, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.42)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 0.248016561337
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.248016561336716, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.25)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 223
\-------------------------
Environment.reset(): Trial set up with start = (5, 2), destination = (1, 7), deadline = 25
0.428527656687
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
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epsilon = 0.4285; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4285; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: right, reward: 0.822565371707
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.8225653717070658, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 0.82)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
1.51864690081
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: left, reward: 2.59313785002
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 2.5931378500223436, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.59)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: left, reward: -10.0127555431
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -10.01275554309822, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.01)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.30877814267
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: None, reward: 1.20942601001
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.2094260100075362, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.21)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: right, reward: 0.203498817725
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.20349881772492362, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.20)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
1.58825880465
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: None, reward: 1.45206610442
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.4520661044198666, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.45)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'left', None)
1.52016245454
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: None, reward: 2.40400265994
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.4040026599421713, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.40)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: None, reward: 1.66273146331
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.6627314633106305, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.66)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'left', None)
1.92494922829
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: left, reward: 1.64106377717
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 1.6410637771658243, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.64)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'forward')
1.24744229481
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 2.36645720749
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.3664572074932444, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.37)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'left')
1.34593911581
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 2.52822636034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.528226360335624, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.53)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.49861139657
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 2.02504842976
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 2.025048429759988, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.03)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'left')
2.13854575522
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 2.73782738653
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 2.73782738653342, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.74)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'forward', 'forward')
2.51590332824
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.45021841605
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.4502184160496148, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.45)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: -9.74806402128
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -9.74806402128242, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent attempted driving left through a red light. (rewarded -9.75)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', 'forward')
2.48306087215
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 1.1320222841
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.1320222841025427, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.13)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'forward', None)
1.47518456986
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 1.15944370195
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.1594437019452146, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.16)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: -4.71857084789
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 8, 't': 17, 'action': None, 'reward': -4.7185708478918835, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.72)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
2.05589237541
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: 0.598890645897
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 18, 'action': 'left', 'reward': 0.5988906458972287, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.60)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
2.01322887976
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 1.94422247188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.9442224718757026, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.94)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: -20.5488383448
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': -20.548838344768807, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.55)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', None, 'left')
1.93708273807
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 1.72993542377
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 4, 't': 21, 'action': None, 'reward': 1.7299354237671223, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.73)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: -20.7718844302
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 3, 't': 22, 'action': 'left', 'reward': -20.77188443024791, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.77)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -0.714999318895
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -0.7149993188945223, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent drove left instead of forward. (rewarded -0.71)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: 0.619803366513
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 1, 't': 24, 'action': 'forward', 'reward': 0.6198033665129219, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 0.62)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 224
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (7, 4), deadline = 25
0.426902341646
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4269; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'left')
0.827560391177
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: forward, reward: 1.39287430957
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 1.3928743095653096, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 1.39)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'forward', 'forward')
0.509709666895
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: 0.280268850788
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 0.2802688507875466, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 0.28)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'right')
1.68135273267
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: 2.72033685615
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 23, 't': 2, 'action': 'left', 'reward': 2.7203368561484846, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.72)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.44423156639
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.2041923631
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.2041923631047233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.20)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None)
2.33830916394
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 2.26962531006
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.2696253100635513, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.27)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'right', 'left')
1.21046861483
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: right, reward: 2.48982781006
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 2.489827810059552, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left')
Agent followed the waypoint right. (rewarded 2.49)
76% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 225
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (2, 4), deadline = 20
0.425283191082
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4253; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'right')
1.7382046783
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 2.15622570671
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.156225706707379, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.16)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: -9.98513781815
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -9.985137818148145, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.99)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 2.18076891174
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.180768911735856, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.18)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
1.0012260426
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 0.839331032595
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 0.8393310325949063, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.84)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
0.965829451114
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 2.08841956728
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.0884195672808117, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.09)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: right, reward: -0.0228075338509
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': -0.022807533850886408, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded -0.02)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 1.11778894294
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.1177889429356676, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.12)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 0.0936109895848
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 0.09361098958483716, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 0.09)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: -9.46204884389
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.462048843894795, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.46)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'forward', None)
1.97946441879
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 2.58182361061
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 2.5818236106067065, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.58)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 1.29716125772
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.2971612577200875, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.30)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 0.91589050717
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.9158905071697527, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent drove right instead of forward. (rewarded 0.92)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 0.883179633546
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.8831796335462834, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.88)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
1.5042119457
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.95870949712
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.9587094971200298, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.96)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.52971590089
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.5297159008864893, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.53)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 0.249277112174
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.24927711217421722, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.25)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', 'right', None)
1.61383749688
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 1.75903965653
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.7590396565280553, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 1.76)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: forward, reward: -9.00156375562
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -9.001563755617392, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.00)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: -10.8434475937
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -10.843447593674256, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.84)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'forward', None)
1.97203308607
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 1.74356789062
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 1.7435678906182732, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.74)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 226
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (3, 4), deadline = 30
0.423670181615
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4237; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
1.97872567582
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 1.94229206553
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.9422920655313967, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.94)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: right, reward: 1.87444772844
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 1.8744477284358343, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.87)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.96208255724
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 1.930298071
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.9302980710044346, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.93)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', 'forward')
1.72247368001
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 1.66359298564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.6635929856411447, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.66)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
1.78300650273
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: left, reward: 1.94291999348
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': 1.94291999347954, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.94)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: right, reward: -19.0310947999
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 25, 't': 5, 'action': 'right', 'reward': -19.03109479987834, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.03)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: right, reward: 0.187264125539
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 0.18726412553904426, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.19)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', 'forward')
1.55646137192
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: 2.60192562372
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 2.6019256237211947, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.60)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
1.76182991316
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 1.58599264148
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': 1.5859926414839864, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.59)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
1.5271245092
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: 2.12598240934
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 2.125982409343292, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.13)
67% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 227
\-------------------------
Environment.reset(): Trial set up with start = (2, 4), destination = (7, 5), deadline = 20
0.422063289953
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4221; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', 'right')
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: 2.8359585817
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'right'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.835958581701691, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'right')
Agent followed the waypoint right. (rewarded 2.84)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
2.44767337041
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 1.95172397217
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.9517239721711708, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.95)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 2.26444381107
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.264443811071474, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.26)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
2.23207124118
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 1.45878003399
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.458780033992266, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.46)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 1.21923784849
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.219237848490242, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.22)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.82655345927
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 2.45825734637
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.4582573463748547, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.46)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', None)
1.33265558293
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 1.49956972177
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.4995697217707016, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.50)
65% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 228
\-------------------------
Environment.reset(): Trial set up with start = (5, 2), destination = (1, 2), deadline = 20
0.420462492892
Simulating trial. . .
epsilon = 0.4205; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4205; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4205; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4205; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4205; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'right')
1.02119206605
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 2.03041434581
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.0304143458090778, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.03)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'left')
1.67888369142
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 1.80329278294
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.803292782935145, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.80)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', 'right')
1.52580320593
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 1.19651834265
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.196518342648494, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.20)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
1.8629632481
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: 0.962197585723
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 0.9621975857228373, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.96)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: right, reward: 1.40821270646
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.4082127064642167, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 1.41)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.63058831115
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: None, reward: 1.03014627719
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.0301462771889165, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.03)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.33036729417
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: None, reward: 2.51970817901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.519708179007674, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.52)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'forward', None)
0.400647706328
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: forward, reward: 0.12930093787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 0.12930093786977293, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.13)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
1.45196577592
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: left, reward: 2.63674757408
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 2.6367475740770714, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.64)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.96050887068
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 0.920217497137
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 0.9202174971374959, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.92)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
1.44036318391
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 1.86500601274
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.8650060127363648, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.87)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.67391127732
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 1.32090841751
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.3209084175054373, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.32)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.49740984741
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 2.50347641176
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 2.5034764117637565, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.50)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'left')
1.90998447508
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.39176724248
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 7, 't': 13, 'action': None, 'reward': 2.3917672424753778, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.39)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'forward', 'forward')
1.80754157812
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 0.677058114198
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.677058114197705, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 0.68)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: -0.372309667503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': -0.37230966750319405, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded -0.37)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: -39.7702333333
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -39.77023333334084, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.77)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', 'left', None)
1.25910207634
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: None, reward: 1.70182077061
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.7018207706083872, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.70)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: -10.8381329629
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -10.838132962930674, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving left through a red light. (rewarded -10.84)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', 'left', None)
2.14240540282
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: forward, reward: 1.6833185507
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': 1.6833185506960795, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.68)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 229
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (8, 2), deadline = 25
0.418867767316
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4189; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
1.41258041691
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: 1.16291772932
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.1629177293166213, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.16)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
1.65268459832
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: None, reward: 1.0802026587
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.080202658702331, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.08)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: -10.8143698168
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -10.814369816815466, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -10.81)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'right')
1.71498932791
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: None, reward: 2.85099795181
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.8509979518110464, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.85)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
2.00044312959
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 1.38773619753
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.3877361975296922, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.39)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.11802596163
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.1180259616310138, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 1.12)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', 'left')
1.76137452439
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: left, reward: 2.61478487827
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 2.6147848782697216, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 2.61)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: forward, reward: 1.03959054196
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.039590541961038, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 1.04)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', 'left')
0.973225948668
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 2.6015144505
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.6015144505013788, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent properly idled at a red light. (rewarded 2.60)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.36644362851
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 1.92211253747
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.9221125374737185, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.92)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'forward')
1.95786871071
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: 1.0318181059
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.0318181059046838, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.03)
56% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 230
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (3, 6), deadline = 20
0.417279090199
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4173; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 1.18436192984
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.1843619298420958, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.18)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 0.524745982109
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.5247459821089936, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 0.52)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', 'forward', 'forward')
0.394989258841
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: forward, reward: 1.23597712411
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.2359771241119222, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 1.24)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
1.32739151066
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: 1.90382313591
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.9038231359096927, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.90)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.91286197676
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 2.15209869638
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.1520986963834496, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.15)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.93677286522
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.9367728652155778, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.94)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'right', None)
1.53263054148
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.80670233776
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.8067023377622258, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.81)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: left, reward: -39.4472550456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -39.44725504557177, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.45)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
1.88514097307
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.23501658774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.235016587738269, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.24)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
1.69408966356
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: 1.08650639644
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.0865063964431658, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.09)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
1.92503773659
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 2.68406929484
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.68406929483767, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.68)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 0.976229612418
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.9762296124179273, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 0.98)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', 'left')
1.24027845839
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 2.30874886066
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.3087488606602835, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.31)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
0.264974322099
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: -0.277157910986
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -0.2771579109862453, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded -0.28)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', 'forward')
2.07919349782
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: left, reward: 0.840158346844
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 0.8401583468439957, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 0.84)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', 'left')
1.77451365953
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 0.955200563728
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.9552005637279437, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 0.96)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: left, reward: -10.5462758855
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -10.546275885451868, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.55)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
0.063700671636
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 0.0278378332232
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.027837833223239516, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.03)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
2.30455351571
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: None, reward: 0.76182826539
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.7618282653900021, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.76)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, 'right')
2.20084479441
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: 2.08649146094
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 2.0864914609351004, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.09)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 231
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (6, 6), deadline = 20
0.415696438598
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4157; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.58621449657
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: right, reward: 2.57143175066
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.571431750657184, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.57)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: None, reward: 2.89236119989
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.8923611998867753, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.89)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
2.18641131168
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: None, reward: 1.47121569983
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.471215699829054, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.47)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
2.03248033657
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: forward, reward: 2.46028495363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 2.4602849536302753, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.46)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'left')
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: forward, reward: 1.4383393779
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.4383393778966638, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 1.44)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.53319089055
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 1.46560105248
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.4656010524784093, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.47)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
1.28774907312
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: left, reward: 1.30165223762
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 1.301652237616382, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.30)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'forward')
1.73716008673
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 1.61492389437
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.6149238943729602, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.61)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'left')
2.15087585878
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 1.18870747218
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.18870747218123, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.19)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.61560732328
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: left, reward: 1.35706949947
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.3570694994711803, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.36)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
2.07882312361
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: right, reward: 1.47115864139
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.4711586413925608, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.47)
45% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 232
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (3, 3), deadline = 20
0.414119789662
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4141; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.49939597151
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 1.06053398746
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.0605339874627788, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.06)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: -10.2683744431
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.268374443063106, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.27)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.27996497949
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 2.33313555129
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.333135551286537, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.33)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
1.48633841138
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: 2.76407454109
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 2.764074541094264, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.76)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.39029803
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 1.78575950562
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.7857595056238833, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.79)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
1.5600787804
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.12596109765
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.1259610976473846, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.13)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.4377549543
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.4377549542960935, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.44)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 1.3438621342
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.343862134201741, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.34)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'right')
1.20497116888
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: None, reward: 0.946214911481
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.9462149114812315, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 0.95)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
2.12520647624
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: left, reward: 1.14836295024
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.1483629502377612, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.15)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 233
\-------------------------
Environment.reset(): Trial set up with start = (8, 3), destination = (2, 5), deadline = 20
0.412549120622
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4125; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'left')
1.74108823718
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 2.02629092514
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.0262909251422014, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.03)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'left')
1.88368958116
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 2.23623006463
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.236230064630421, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.24)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 2.66942345336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.6694234533596664, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.67)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
2.30780688374
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 2.79860216426
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.7986021642550623, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.80)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.42271892969
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.4227189296898497, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.42)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: left, reward: -9.25507678701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': -9.255076787008147, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.26)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
0.847083253705
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 2.48429419787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 2.484294197866996, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.48)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.42002849455
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.4200284945535593, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.42)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', 'left')
1.82267429307
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.77875611667
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.77875611667481, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.78)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 1.02507615773
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.025076157734675, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent drove right instead of forward. (rewarded 1.03)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'right', None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: None, reward: 2.55341589258
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.553415892579493, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.55)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'right', None)
2.27498217981
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: None, reward: 2.19407355602
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.194073556022362, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.19)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 0.282099842565
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 0.2820998425647556, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.28)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
1.63678471324
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: left, reward: 1.40363136172
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.4036313617154268, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.40)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'left')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: 2.54618077747
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 2.546180777473877, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.55)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
1.82881350575
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 0.977994386028
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.9779943860283853, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.98)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: -0.338744046912
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': -0.33874404691218696, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded -0.34)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: -19.013406808
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': -19.013406808038447, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.01)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: -0.0848399382924
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': -0.08483993829240166, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded -0.08)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', 'forward', None)
1.85780048835
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 1.9896584571
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 1.9896584571035776, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.99)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 234
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (2, 2), deadline = 30
0.410984408799
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4110; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
1.52020803748
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: left, reward: 2.80295996148
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 2.8029599614796807, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.80)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 1.24741320616
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 1.2474132061611707, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.25)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
1.3173141359
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.40469017816
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.4046901781637293, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.40)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'right', None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: forward, reward: -10.1679609926
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': -10.167960992605488, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -10.17)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: forward, reward: -40.0537038622
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': -40.053703862239736, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.05)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', 'forward')
0.815483191477
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: forward, reward: 1.78391076625
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 1.7839107662537539, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 1.78)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: None, reward: 2.68005696629
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.6800569662853575, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.68)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: None, reward: -5.55844985597
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 23, 't': 7, 'action': None, 'reward': -5.558449855974587, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.56)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: 0.294446160376
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': 0.29444616037641735, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 0.29)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: left, reward: 1.12483109171
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 1.1248310917124773, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.12)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.58802876781
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 2.71462769295
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 2.7146276929503212, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.71)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: -20.6599521759
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 19, 't': 11, 'action': 'right', 'reward': -20.659952175940624, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.66)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 1.98351515601
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.9835151560135627, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.98)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: left, reward: 0.925171742377
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 17, 't': 13, 'action': 'left', 'reward': 0.9251717423769772, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.93)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'right', 'forward')
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: forward, reward: -0.0850213892398
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': -0.08502138923975044, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent drove forward instead of right. (rewarded -0.09)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', None)
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: left, reward: -9.68536951423
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -9.685369514234843, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.69)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', 'left', None)
1.17354490554
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: right, reward: 0.866215767637
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 0.8662157676366411, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 0.87)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'forward')
2.14680158556
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: None, reward: 2.57325513798
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 13, 't': 17, 'action': None, 'reward': 2.573255137984663, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.57)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
1.7749908825
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: right, reward: 1.88638380726
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 18, 'action': 'right', 'reward': 1.886383807260715, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.89)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: forward, reward: -9.01674894383
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 19, 'action': 'forward', 'reward': -9.016748943826833, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.02)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None)
1.40340394589
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: None, reward: 1.91099271881
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 20, 'action': None, 'reward': 1.910992718806318, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.91)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
2.2463826451
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: forward, reward: 0.644652130523
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 0.6446521305229864, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.64)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
2.15132823038
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: 2.00749889622
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 22, 'action': 'forward', 'reward': 2.0074988962241833, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.01)
23% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 235
\-------------------------
Environment.reset(): Trial set up with start = (2, 4), destination = (5, 2), deadline = 25
0.409425631598
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4094; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: forward, reward: -10.2776066935
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': -10.277606693514608, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.28)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', 'forward')
1.24229984616
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 1.0162369536
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.0162369535988802, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.02)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: forward, reward: -10.0804258491
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -10.080425849109377, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.08)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 0.801784749355
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.8017847493549466, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.80)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
1.65719833235
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 2.22216566212
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.222165662119096, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.22)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.44551738781
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 2.2511188989
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.251118898902287, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.25)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
1.84301993903
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.00212382478
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.002123824776737, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.00)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
2.03215328877
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.66189101875
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.661891018747303, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.66)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
2.34702215376
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.18226953635
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.1822695363451179, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.18)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.76464584505
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.03026032678
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.030260326784439, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.03)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.81318408996
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.8131840899602774, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.81)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
2.0794135633
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 1.9447791281
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 1.944779128098844, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.94)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'left', None)
1.84831814336
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: 1.25838006681
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 1.2583800668069995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.26)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', 'right')
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 1.53479058825
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 1.5347905882547475, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent drove right instead of forward. (rewarded 1.53)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', None)
1.20976587354
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: left, reward: 0.818847382151
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': 0.8188473821512647, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.82)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 2.61865305586
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 2.6186530558636067, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.62)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
1.66568872579
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 1.12677506815
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 1.1267750681521824, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.13)
32% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 236
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (2, 3), deadline = 25
0.407872766511
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4079; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: left, reward: 2.65773243379
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 2.6577324337858204, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.66)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: forward, reward: 0.563781162445
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 0.5637811624448885, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.56)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
2.553204524
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.41997106005
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.41997106005482, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.42)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.98658779203
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 2.60892506579
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.608925065792658, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.61)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
2.29775642891
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.34289276529
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.3428927652851652, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.34)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: forward, reward: -10.5557591588
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -10.555759158762562, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.56)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: left, reward: 1.66668352237
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 1.6666835223715923, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.67)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: left, reward: -9.57315188968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': -9.573151889680927, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.57)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
1.32421196475
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: 2.37938143765
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.3793814376464564, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.38)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', 'left')
1.69361904784
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: forward, reward: 1.30314815363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.303148153628939, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.30)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
1.55334910508
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: 1.15779818413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.1577981841259222, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.16)
56% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 237
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (8, 2), deadline = 25
0.406325791113
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4063; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: -5.16029876429
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 25, 't': 0, 'action': None, 'reward': -5.1602987642893465, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.16)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'right', None)
1.41611265235
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: forward, reward: 1.80739492968
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.8073949296800325, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.81)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.89745308592
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: None, reward: 2.10414188053
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.104141880526102, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.10)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
2.0120963457
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: forward, reward: 1.99552231872
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 1.99552231871862, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.00)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
2.00079748322
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 1.14021226881
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.1402122688052099, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.14)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
2.00380933221
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 1.12788330349
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.127883303490506, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.13)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', 'left')
2.18807970133
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: left, reward: 2.60817703186
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 2.608177031860937, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 2.61)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
1.3555736446
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: forward, reward: 2.4407029114
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 2.440702911395782, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.44)
68% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 238
\-------------------------
Environment.reset(): Trial set up with start = (5, 7), destination = (1, 4), deadline = 35
0.404784683066
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4048; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 1.61350203646
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 35, 't': 0, 'action': 'forward', 'reward': 1.6135020364595203, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.61)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 0.939319294146
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 34, 't': 1, 'action': 'right', 'reward': 0.9393192941458242, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent drove right instead of forward. (rewarded 0.94)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'right', 'right')
1.06681017491
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 2.25875546104
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'right'), 'deadline': 33, 't': 2, 'action': None, 'reward': 2.2587554610381506, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 2.26)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'left')
1.66979166548
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 2.08813762501
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 32, 't': 3, 'action': None, 'reward': 2.088137625009963, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.09)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: -9.77504735307
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 31, 't': 4, 'action': 'left', 'reward': -9.775047353065924, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.78)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
2.16158399948
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: left, reward: 1.41085577118
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 30, 't': 5, 'action': 'left', 'reward': 1.4108557711814087, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.41)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: right, reward: 0.0391735648295
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 29, 't': 6, 'action': 'right', 'reward': 0.03917356482951195, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.04)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: right, reward: 0.668113234363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 28, 't': 7, 'action': 'right', 'reward': 0.668113234363141, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.67)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'left')
1.87896464524
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 1.41409927113
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 27, 't': 8, 'action': None, 'reward': 1.4140992711319256, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.41)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
1.80655026539
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 1.92477366314
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 26, 't': 9, 'action': None, 'reward': 1.9247736631354082, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.92)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'right')
2.14366812767
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: left, reward: 2.65309351222
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 25, 't': 10, 'action': 'left', 'reward': 2.653093512224822, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.65)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: None, reward: -4.40125687764
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 11, 'action': None, 'reward': -4.401256877635093, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.40)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'forward', None)
0.0457692524296
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: right, reward: 0.865393336895
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 23, 't': 12, 'action': 'right', 'reward': 0.8653933368947847, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.87)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, 'right')
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: -40.840520879
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 22, 't': 13, 'action': 'left', 'reward': -40.840520878991384, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.84)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, 'forward')
2.36002836177
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 1.62897767897
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 21, 't': 14, 'action': None, 'reward': 1.62897767897374, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.63)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'left')
1.94098445934
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: right, reward: 2.68943373298
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 20, 't': 15, 'action': 'right', 'reward': 2.689433732981074, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.69)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, 'left')
1.77440000796
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: right, reward: 1.85902455121
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 19, 't': 16, 'action': 'right', 'reward': 1.8590245512135644, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.86)
51% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
1.56584631785
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: 1.38709689562
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 17, 'action': 'forward', 'reward': 1.3870968956232235, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.39)
49% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', 'right', 'left')
2.04030164873
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 1.82990156888
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 17, 't': 18, 'action': 'forward', 'reward': 1.8299015688824494, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 1.83)
46% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, 'forward')
1.62235235272
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 2.41683338479
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 16, 't': 19, 'action': None, 'reward': 2.4168333847887906, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.42)
43% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: -9.28648969234
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 20, 'action': 'forward', 'reward': -9.286489692344631, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.29)
40% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
1.47647160674
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 0.986456757413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 21, 'action': 'forward', 'reward': 0.9864567574127159, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.99)
37% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, None)
1.86953126431
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 0.818169849786
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 22, 'action': None, 'reward': 0.8181698497863259, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.82)
34% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, 'right')
1.9472151925
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 1.96604496885
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 12, 't': 23, 'action': None, 'reward': 1.9660449688456871, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.97)
31% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'red', None, None)
1.83068734488
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: right, reward: 1.31724869636
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 24, 'action': 'right', 'reward': 1.3172486963576686, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.32)
29% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 239
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (4, 7), deadline = 30
0.403249420118
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4032; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', None)
1.92372947272
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: 2.07893555249
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 2.078935552494768, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.08)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: left, reward: 1.50698780734
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 29, 't': 1, 'action': 'left', 'reward': 1.506987807339562, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.51)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
1.84542563759
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 2.8739098636
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.8739098636041693, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.87)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
2.3596677506
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 2.53810456088
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.538104560878338, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.54)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: right, reward: 2.60897923001
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 2.6089792300140093, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.61)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: 1.59109865047
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 5, 'action': 'left', 'reward': 1.591098650469346, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.59)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.79055012996
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.790550129963778, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.79)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
1.57396802062
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 1.4190061465
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 1.4190061464966572, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.42)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'forward')
1.67604199055
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 2.34583215679
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.3458321567906055, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.35)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 1.77446298308
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 1.7744629830841878, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.77)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, 'right')
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: -4.98989282023
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 20, 't': 10, 'action': None, 'reward': -4.989892820228383, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.99)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 2.11224836517
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 2.1122483651707666, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.11)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'right', None)
2.04376552131
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.93959711346
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.9395971134595325, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.94)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'right', None)
1.6864385767
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 2.71523595684
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 2.7152359568414655, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 2.72)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'right', None)
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 0.847120803743
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 0.8471208037426006, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 0.85)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'right')
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: left, reward: -10.6452834214
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -10.645283421412518, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving left through a red light. (rewarded -10.65)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
1.86566196426
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 0.773737148238
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 16, 'action': None, 'reward': 0.7737371482378073, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.77)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
1.11413711147
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.43322168319
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 13, 't': 17, 'action': None, 'reward': 1.4332216831923366, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.43)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
1.78621988533
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: 2.27591474145
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 18, 'action': 'left', 'reward': 2.2759147414469023, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.28)
37% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 240
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (3, 5), deadline = 30
0.401719980098
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4017; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', None)
1.8517967012
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: None, reward: 1.99744805258
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.9974480525799712, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.00)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: left, reward: -9.247610581
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 29, 't': 1, 'action': 'left', 'reward': -9.247610581002098, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.25)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: None, reward: 2.41834453081
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.4183445308086142, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.42)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: left, reward: -9.08789181671
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': 'left', 'reward': -9.08789181671127, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.09)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: 1.81343958743
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 1.813439587433269, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.81)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.31969955625
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 1.09713079892
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.0971307989177654, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.10)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
2.03106731339
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: left, reward: 2.41265556513
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 2.4126555651301, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.41)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.57050487601
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.1691223631
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.1691223631033594, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.17)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.36981361956
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.80644219381
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.8064421938080262, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.81)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.58812790668
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.14412804705
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.144128047050212, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.14)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
1.86612797687
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.73731018247
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.7373101824676238, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.74)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'left')
2.43818657088
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: 1.53582197707
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.535821977071087, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.54)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: 2.00343661816
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 2.0034366181588674, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.00)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: -4.49811099478
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 17, 't': 13, 'action': None, 'reward': -4.4981109947815145, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.50)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
1.61745040012
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: 1.91905855919
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': 1.9190585591910596, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.92)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 1.14340718451
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.1434071845109448, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.14)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 0.852819065189
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 0.8528190651894263, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.85)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: -4.62531028183
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 17, 'action': None, 'reward': -4.625310281829972, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.63)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: 0.526609115773
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': 0.5266091157730364, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.53)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, 'left')
2.29526872624
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: left, reward: 1.29285246974
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 11, 't': 19, 'action': 'left', 'reward': 1.2928524697416677, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.29)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'left', None)
1.8203245971
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 1.40015425379
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 20, 'action': None, 'reward': 1.4001542537893208, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.40)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 0.278018150431
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 9, 't': 21, 'action': None, 'reward': 0.2780181504312579, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 0.28)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, 'left')
1.79406059799
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: left, reward: 2.27852006247
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 8, 't': 22, 'action': 'left', 'reward': 2.278520062468796, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.28)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 0.503387952736
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 7, 't': 23, 'action': None, 'reward': 0.50338795273586, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.50)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: -39.5665433968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 24, 'action': 'forward', 'reward': -39.56654339676593, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.57)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: -0.312776863896
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 25, 'action': 'right', 'reward': -0.3127768638963131, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded -0.31)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: left, reward: 1.41596883275
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 4, 't': 26, 'action': 'left', 'reward': 1.415968832746034, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 1.42)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'red', None, None)
1.49648708356
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: 0.644605306929
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 27, 'action': 'right', 'reward': 0.6446053069291102, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.64)
7% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 241
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (5, 2), deadline = 25
0.400196340921
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
Simulating trial. . .
epsilon = 0.4002; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
2.22186143926
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: 2.99115282248
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 2.9911528224814794, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.99)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'left')
1.9225718819
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.78612128004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.7861212800429516, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.79)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.17150123871
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.1715012387061163, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.17)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 0.0604620606781
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.060462060678142304, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.06)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 1.51289357703
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.5128935770285827, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.51)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'left', None)
2.44888615574
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 2.02354952492
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.023549524921399, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.02)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', 'forward')
1.71069670601
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 1.9083426219
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.9083426218989563, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.91)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'right', None)
1.99168131738
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 2.74554249232
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.7455424923205545, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.75)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, 'forward')
2.39252657517
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 1.37505406385
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.375054063854303, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.38)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 1.8873194957
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.8873194956995034, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.89)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 2.64500128129
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.645001281294035, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.65)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: -10.9981498642
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': -10.998149864206555, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -11.00)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
1.93968199723
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.74481146365
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.7448114636506036, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.74)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 0.656243161125
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': None, 'reward': 0.6562431611246551, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.66)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 0.193756727682
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.19375672768152563, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.19)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', 'forward')
1.56317972235
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: 1.23856070231
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 1.2385607023098741, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.24)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: -9.74162389157
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': -9.741623891566347, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.74)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
1.898138278
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.96605706973
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.9660570697269837, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.97)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
1.17591118105
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 0.82065320396
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 0.8206532039598036, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.82)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
0.998282192504
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 0.668473564218
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 0.6684735642176307, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.67)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'right', None)
2.23452786792
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 0.888635560838
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 0.8886355608379604, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.89)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None)
2.60650713087
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: left, reward: 2.20804519885
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 2.2080451988524517, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.21)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: forward, reward: -9.57267095513
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': -9.572670955132816, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.57)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', 'left', None)
1.93209767386
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: forward, reward: 1.61494070208
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 2, 't': 23, 'action': 'forward', 'reward': 1.6149407020844597, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.61)
4% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 242
\-------------------------
Environment.reset(): Trial set up with start = (3, 4), destination = (8, 3), deadline = 20
0.398678480587
Simulating trial. . .
epsilon = 0.3987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3987; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'left')
2.05995982289
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 1.54935599283
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.5493559928309655, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.55)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: left, reward: -10.2711006917
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.27110069169077, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.27)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.61023942544
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 1.38638793834
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.3863879383366307, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.39)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.49831368189
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 2.84925850353
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.849258503529911, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.85)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
2.17378609271
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 1.16595998644
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.16595998643661, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.17)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
1.27367939733
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 1.06422131072
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.0642213107210763, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.06)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
1.16895035403
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 0.0486934006191
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 0.04869340061914085, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.05)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
2.15563259144
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: left, reward: 1.89989896417
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.8998989641702213, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.90)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', 'right')
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: right, reward: 0.40650750999
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 0.40650750999027274, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent drove right instead of forward. (rewarded 0.41)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'forward')
2.02776577781
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: 1.15333004727
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.1533300472705494, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.15)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: -40.0714551803
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -40.071455180266895, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.07)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', None)
2.34224673044
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 2.57295795632
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.572957956315797, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.57)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
2.45760234338
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 2.46369697697
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.463696976970755, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.46)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: 0.679713561401
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 0.6797135614009534, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.68)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', None)
2.23621784033
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 1.5087907888
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.508790788797479, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.51)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'left', 'right')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 1.59755070224
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.5975507022408655, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.60)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'left', None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: -10.7308717442
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -10.730871744212266, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.73)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'left', None)
1.8144297833
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 0.803476138053
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.8034761380530007, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 0.80)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 0.974667172281
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.9746671722812004, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.97)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 0.769259276599
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.7692592765991213, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.77)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 243
\-------------------------
Environment.reset(): Trial set up with start = (5, 7), destination = (2, 6), deadline = 20
0.397166377177
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3972; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.34385055705
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 2.11946457807
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.1194645780714767, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.12)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
1.73165756756
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 2.3601064736
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.3601064735977726, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.36)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: forward, reward: -10.9294986025
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.929498602465102, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.93)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'left', None)
1.30895296068
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: right, reward: 2.41918498838
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.419184988379139, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.42)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: 1.01147812003
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 1.0114781200262226, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 1.01)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, 'forward')
1.88379031951
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 2.03481445009
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.0348144500890353, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.03)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 1.31484600458
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.3148460045765764, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.31)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.98661015919
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.22217492409
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.2221749240885194, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.22)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: left, reward: -10.1283270818
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -10.12832708179202, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.13)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.60439254164
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.17908374482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.1790837448179146, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.18)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: -5.85641271128
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 10, 't': 10, 'action': None, 'reward': -5.856412711282315, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.86)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'left')
1.98700427398
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 1.05219200883
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.0521920088330998, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.05)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.9420280144
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.9420280144037867, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.94)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
1.39173814323
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 2.46159361885
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 2.4615936188479877, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.46)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
1.92666588104
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 2.40169367155
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.4016936715464237, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.40)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: -10.8168890893
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -10.81688908932038, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.82)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
1.77351918797
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: 1.55275856494
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 1.5527585649375122, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.55)
15% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 244
\-------------------------
Environment.reset(): Trial set up with start = (3, 6), destination = (6, 4), deadline = 25
0.395660008856
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3957; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
2.04588202058
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 2.33124923946
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.3312492394556537, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.33)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
2.2806440147
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: right, reward: 2.25928668522
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 2.2592866852208973, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.26)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: forward, reward: 1.2478304158
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 1.2478304157991917, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.25)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
1.49484340831
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: 1.49436184546
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 1.4943618454606797, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.49)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', 'forward')
1.69303333283
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 2.13927158821
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.1392715882064604, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.14)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
1.01430662785
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: left, reward: 1.71855517548
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 1.7185551754812294, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.72)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
2.35434658097
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 1.45982304118
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.4598230411753335, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.46)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, 'right')
2.00101874015
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: forward, reward: 1.77840743946
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.7784074394588119, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 1.78)
68% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 245
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (7, 5), deadline = 20
0.394159353872
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3942; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
2.16417977629
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.85372927858
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.853729278576986, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.85)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: right, reward: 0.764664590153
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.7646645901531194, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.76)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.66987303957
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 1.7631272988
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.7631272988011477, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.76)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.71650016919
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 2.50245868358
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.5024586835840728, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.50)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
2.10947942639
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 2.01406886795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.014068867947598, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.01)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 0.492780687511
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.49278068751054405, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.49)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: -20.7511887362
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': -20.751188736215067, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.75)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'right', 'left')
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 1.1093603709
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.1093603708972153, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove forward instead of left. (rewarded 1.11)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'right', None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: left, reward: -20.2155653726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -20.21556537256936, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.22)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 1.26235366295
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.2623536629545404, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.26)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: forward, reward: -0.212651364895
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -0.2126513648946854, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded -0.21)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', 'right')
1.83290178253
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 1.11275504423
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.1127550442344558, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.11)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', 'left')
1.48204269206
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.30915651531
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.3091565153115785, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.31)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
1.66313887646
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: 2.50260576647
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 2.5026057664695047, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.50)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: left, reward: 1.11210679816
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 1.1121067981595467, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 1.11)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None)
1.82077981338
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 2.36681180442
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 2.3668118044181594, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.37)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'forward')
1.49460262689
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: 2.3214406451
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 2.3214406451019123, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.32)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 0.897260066135
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.8972600661351434, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.90)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'forward')
1.90802163599
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: 0.435961246547
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 0.4359612465470972, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.44)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: -4.84830358652
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': -4.848303586519435, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.85)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 246
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (4, 3), deadline = 30
0.392664390557
Simulating trial. . .
epsilon = 0.3927; alpha = 0.5000
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Simulating trial. . .
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epsilon = 0.3927; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3927; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3927; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3927; alpha = 0.5000
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epsilon = 0.3927; alpha = 0.5000
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Simulating trial. . .
epsilon = 0.3927; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', None)
2.26996534996
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: right, reward: 1.90079922278
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.9007992227847157, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.90)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'left', 'right')
1.47282841338
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: right, reward: 2.79250938199
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 2.792509381992917, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.79)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
2.20662788047
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 1.9048260388
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': 1.9048260387993166, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.90)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'forward')
1.26149041075
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.25160810637
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.2516081063660864, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.25)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'right', None)
2.16966643962
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.37924113618
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.3792411361753718, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.38)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: right, reward: 0.224729594252
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 0.22472959425197037, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.22)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
0.838733636255
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: right, reward: 0.419113844065
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 0.41911384406461305, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.42)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, 'forward')
1.9593023848
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 2.72069336815
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 23, 't': 7, 'action': None, 'reward': 2.7206933681538645, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.72)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
2.18856563002
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 1.14164333278
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.1416433327813094, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.14)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 0.0481162508034
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 0.04811625080341142, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.05)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: right, reward: 1.74203692578
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 1.7420369257816886, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.74)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
1.91791636734
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: right, reward: 1.82061449059
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 1.8206144905944643, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.82)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: -10.7651765824
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': -10.76517658242983, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.77)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
2.08287232146
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 2.54913318575
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 2.5491331857536212, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.55)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
2.05572695964
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 0.807819252537
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': 0.8078192525371555, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.81)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: 1.23050113576
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'left', 'reward': 1.230501135764433, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.23)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: -0.163453037936
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': -0.16345303793603727, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded -0.16)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: -9.96737753901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': -9.96737753901472, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.97)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
0.833377878361
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 2.50583575714
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 18, 'action': None, 'reward': 2.5058357571350767, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.51)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, 'right')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: 1.24733187038
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 11, 't': 19, 'action': 'left', 'reward': 1.247331870382156, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.25)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'green', 'right', 'left')
0.760998054233
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: -0.249740024488
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 10, 't': 20, 'action': 'right', 'reward': -0.24974002448837362, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove right instead of left. (rewarded -0.25)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: left, reward: 1.41189055566
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 9, 't': 21, 'action': 'left', 'reward': 1.4118905556622456, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove left instead of right. (rewarded 1.41)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'red', 'left', None)
2.06177414717
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 1.93056627832
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 8, 't': 22, 'action': None, 'reward': 1.9305662783195585, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.93)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', 'left', None)
1.99617021274
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 0.749936113281
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 7, 't': 23, 'action': None, 'reward': 0.7499361132811215, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.75)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: -19.0894918224
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 6, 't': 24, 'action': 'right', 'reward': -19.089491822373134, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.09)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 0.317114706421
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 5, 't': 25, 'action': None, 'reward': 0.31711470642053996, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.32)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', 'left', 'forward')
1.40087021233
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: left, reward: 1.04454470886
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 4, 't': 26, 'action': 'left', 'reward': 1.0445447088629605, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.04)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'red', None, None)
2.50895452743
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 0.329112805785
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 27, 'action': None, 'reward': 0.32911280578545954, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.33)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('forward', 'red', None, 'right')
1.97321924395
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 1.31558331396
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 2, 't': 28, 'action': None, 'reward': 1.3155833139575592, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.32)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'green', None, None)
1.43177310609
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: forward, reward: 0.532902886688
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 29, 'action': 'forward', 'reward': 0.5329028866877945, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.53)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 247
\-------------------------
Environment.reset(): Trial set up with start = (4, 6), destination = (1, 7), deadline = 20
0.391175097322
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3912; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'right')
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: 1.01686061812
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.0168606181157736, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent followed the waypoint forward. (rewarded 1.02)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: None, reward: -5.2110638572
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': -5.211063857200059, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.21)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: 0.717178414625
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': 0.7171784146252175, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.72)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'left', 'forward')
1.80951966395
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 2.91572807899
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.9157280789920943, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 2.92)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: -0.0479485903332
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': -0.047948590333244945, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded -0.05)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward')
2.01093707367
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 2.66590129084
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.665901290842492, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.67)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: -9.16275067125
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -9.162750671249812, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.16)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: right, reward: 0.27497019702
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 0.2749701970198768, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 0.27)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: -10.2934158004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -10.293415800367152, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.29)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: forward, reward: 0.318246034998
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 0.31824603499849813, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.32)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'right', None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: left, reward: -20.3055879976
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -20.30558799761198, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.31)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 1.84981844963
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.8498184496284493, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.85)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: forward, reward: -10.7386622868
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -10.738662286800789, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.74)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: -4.00076248115
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': -4.00076248114669, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.00)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 1.45734880736
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.4573488073570786, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.46)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.75746146551
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 1.67455750606
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.6745575060593982, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.67)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
1.71600948579
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 1.36522738595
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.3652273859459199, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.37)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: 0.33830158748
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 0.3383015874796743, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.34)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
1.66960681775
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 1.93659884736
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.9365988473590299, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.94)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', None)
1.37305316301
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 0.765952891195
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.7659528911950926, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.77)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 248
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (8, 5), deadline = 30
0.389691452662
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3897; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
1.81671227959
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: right, reward: 1.01127698498
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.0112769849785412, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.01)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
2.17148345385
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 2.57052505819
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.5705250581864654, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.57)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
2.46064966017
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 1.16102459608
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.1610245960836538, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.16)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.81083712813
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 1.78423454822
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.7842345482225759, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.78)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
2.31600275361
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: 2.59849623165
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 2.5984962316526152, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.60)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
2.37100425602
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.34106253869
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 2.341062538691879, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.34)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 1.04093013854
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 1.0409301385377738, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 1.04)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
1.79753583818
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.34579207616
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 7, 'action': None, 'reward': 2.3457920761630517, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.35)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.12916339404
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.1291633940437196, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.13)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
2.45724949263
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 2.51206852904
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 2.512068529035208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.51)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: -9.5283106558
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': -9.528310655803311, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.53)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: -9.28340112784
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': -9.283401127840653, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.28)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', 'forward')
2.27145473394
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 1.07982566648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.0798256664848624, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.08)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 1.36072626341
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 1.360726263406179, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 1.36)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
1.54061843587
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.40235016456
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.402350164562834, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.40)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.47148430021
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.89781597722
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.8978159772177126, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.90)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: -10.5779325763
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 14, 't': 16, 'action': 'left', 'reward': -10.577932576317322, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.58)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, 'left')
2.31520909616
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 1.812792543
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 1.8127925430013319, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.81)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', 'forward', 'forward')
1.12916646029
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: 1.56374308593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 12, 't': 18, 'action': 'right', 'reward': 1.5637430859336368, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 1.56)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: left, reward: 1.41227604867
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 19, 'action': 'left', 'reward': 1.4122760486712729, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.41)
33% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 249
\-------------------------
Environment.reset(): Trial set up with start = (4, 5), destination = (5, 2), deadline = 20
0.388213435154
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3882; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', None)
1.36100215703
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: None, reward: 1.21824344066
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.2182434406565092, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.22)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: right, reward: 1.58172044799
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.5817204479920646, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.58)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'forward')
1.59054791254
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: left, reward: 2.02615513252
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': 2.0261551325226117, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.03)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 1.52261325158
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.52261325157906, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.52)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
1.66285804207
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 2.72465175349
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.7246517534946153, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.72)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
1.36643090166
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: left, reward: 1.30512736781
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.3051273678124558, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.31)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: 0.924315275494
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.9243152754944839, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.92)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 2.74939852901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.7493985290109695, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.75)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
2.4715767134
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 2.45513964608
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.4551396460773356, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.46)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: forward, reward: 0.955722827631
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 0.9557228276310803, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.96)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: -9.07702297467
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -9.077022974667873, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.08)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'right')
1.07559304018
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.14600722481
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.1460072248105624, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.15)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 2.13607846878
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.136078468778847, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.14)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: forward, reward: 1.18385810899
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.183858108986938, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.18)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', 'forward')
1.2227074606
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: left, reward: 0.730402694724
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 0.7304026947241793, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 0.73)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'forward')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: left, reward: 0.564269473601
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 0.5642694736013014, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 0.56)
20% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 250
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (3, 5), deadline = 20
0.386741023455
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3867; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: -10.3666301271
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -10.366630127059782, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.37)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: -10.3708139704
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.370813970390243, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.37)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
1.68465013872
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.10126994298
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.1012699429774209, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.10)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, 'right')
1.95663008067
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.38354778326
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.3835477832563456, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.38)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
1.86926542897
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 2.67250020298
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 2.672500202980921, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.67)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
1.39296004085
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 2.57114085082
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.571140850818481, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.57)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'right', None)
2.36861190485
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.27937750959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.279377509591247, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.28)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 1.76833592209
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.7683359220882362, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.77)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', 'left')
1.78737019958
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 0.899278510466
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.8992785104664065, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent properly idled at a red light. (rewarded 0.90)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: -9.35396390122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -9.35396390122452, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -9.35)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', None)
2.303967237
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: forward, reward: 1.2798443345
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.279844334502827, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.28)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: left, reward: -19.2669165811
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -19.26691658110291, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.27)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: right, reward: 1.49667272079
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.496672720793726, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 1.50)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
2.29971832426
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 0.857206212633
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.8572062126325619, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.86)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', 'forward')
1.91615246052
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 1.71535542261
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.715355422612803, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.72)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', None)
1.33577913474
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: left, reward: 2.2201492726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 2.2201492726046936, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.22)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'forward', None)
1.28962279884
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 1.61209205875
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.6120920587547907, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.61)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', 'forward')
1.29969697887
Environment.act() [POST]: location: (4, 4), heading: (0, -1), action: forward, reward: 1.27756318877
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 1.277563188765293, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 1.28)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 4), heading: (0, -1), action: None, reward: 1.36422941483
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.3642294148333798, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.36)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', 'left')
1.80465790786
Environment.act() [POST]: location: (4, 4), heading: (0, -1), action: None, reward: 0.791343594341
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.7913435943410376, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 0.79)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 251
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (4, 2), deadline = 20
0.385274196302
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3853; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'left')
1.2980007511
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: None, reward: 1.38518153899
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.3851815389934956, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.39)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'right')
1.36116077429
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: None, reward: 2.00368350709
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.0036835070887715, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.00)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 1.88193715626
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.881937156256463, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.88)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: forward, reward: -10.9030059968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.903005996844517, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.90)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'left', 'right')
2.13266889769
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: right, reward: 1.04159159728
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.0415915972824965, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.04)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 1.14731945576
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.147319455760018, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.15)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
0.982337996387
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 1.48311731996
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.4831173199600676, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.48)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.57846226845
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.97336633482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.9733663348219364, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.97)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.77591430163
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.51640102674
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.516401026742108, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.52)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.61558234949
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: 1.95484459057
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.95484459057126, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.95)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: -4.96681632551
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': -4.9668163255086615, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.97)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None)
2.35603339735
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 1.93137433336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.9313743333566582, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.93)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'right', 'right')
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: -40.6351469747
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -40.635146974672125, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.64)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', 'left')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: forward, reward: 2.11314652173
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 2.113146521729892, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 2.11)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 252
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (3, 3), deadline = 25
0.383812932516
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3838; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
1.78521347003
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 1.83152914409
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 1.8315291440908084, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.83)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.02393986076
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.0239398607649395, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.02)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: -10.6418628014
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -10.641862801378194, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.64)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.22148676369
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.67245916214
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.6724591621356137, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.67)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.94697296291
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.21103214648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.211032146478625, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.21)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
2.0790025547
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.79641719512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.796417195121642, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.80)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: -4.50576299304
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': -4.505762993040287, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.51)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: left, reward: 0.557466415382
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 0.5574664153816933, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.56)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', 'left', 'forward')
2.36262387147
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 1.37373490363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.3737349036272481, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.37)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.60041367561
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.45953786087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.45953786087256, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.46)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
2.48465901083
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 1.58154967576
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.5815496757615313, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.58)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'forward')
1.25654925856
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.14848199356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.1484819935610178, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.15)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.39011321756
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.3901132175586035, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.39)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'right', 'forward')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: -19.334226642
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': -19.334226641978713, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.33)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'forward')
1.70251562606
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.30894879758
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 11, 't': 14, 'action': None, 'reward': 2.308948797575824, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.31)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: -19.5166733145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 10, 't': 15, 'action': 'right', 'reward': -19.51667331451027, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.52)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 0.912432408097
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 0.9124324080968814, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.91)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
2.0331043433
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 1.59497526605
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.594975266054909, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.59)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 253
\-------------------------
Environment.reset(): Trial set up with start = (1, 6), destination = (6, 7), deadline = 20
0.382357210995
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3824; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
2.01960936903
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 2.18025271473
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.1802527147295807, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.18)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
2.02997576824
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: None, reward: 2.0167738638
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.016773863804338, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.02)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
2.02337481602
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: None, reward: 1.07802513802
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.0780251380155967, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.08)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 1.94670769405
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.946707694049099, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.95)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', 'forward')
1.12926839988
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 2.40049997562
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.400499975620657, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.40)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', 'forward')
1.76488418775
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 2.65287312775
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.65287312775462, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.65)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'forward', None)
1.4508574288
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 1.76550153961
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.7655015396128362, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.77)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', None)
1.77796420367
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: 2.18263993031
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.182639930305052, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.18)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 1.59733519501
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.597335195011614, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.60)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.80837130706
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: left, reward: 2.76804458131
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 2.7680445813127212, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.77)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 0.870184442113
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.8701844421128241, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 0.87)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'forward')
1.18631049807
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: left, reward: 0.73256887605
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 0.732568876049662, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 0.73)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'forward')
0.959439687058
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: left, reward: 2.33985689442
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 2.339856894419588, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.34)
35% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 254
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (3, 4), deadline = 20
0.380907010719
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3809; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: left, reward: -39.9726970036
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -39.972697003596735, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.97)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: forward, reward: -40.6384095188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -40.6384095188232, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.64)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.36657843047
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 0.665917297399
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 0.6659172973993116, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.67)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: left, reward: 0.61777501008
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 0.6177750100803033, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 0.62)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
1.98205044583
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: None, reward: 1.46074446883
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.4607444688289148, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.46)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: None, reward: 0.192397146779
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 0.19239714677924757, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.19)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: None, reward: -5.05818139316
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 14, 't': 6, 'action': None, 'reward': -5.058181393162785, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.06)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'right', 'forward')
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: left, reward: -40.6708340113
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -40.67083401126469, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.67)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
1.72139745733
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: None, reward: 0.889312690639
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.8893126906386253, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.89)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
2.09993104188
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: right, reward: 1.10574778642
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.1057477864204766, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.11)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
2.41391154623
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 0.922010502808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 0.9220105028080605, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.92)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 1.39833664513
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.3983366451339752, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.40)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'left', None)
1.81403980468
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 2.01054098927
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 2.010540989267278, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.01)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'right', None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: 1.88584610913
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.885846109132673, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.89)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 255
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (5, 5), deadline = 30
0.379462310746
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
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epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3795; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 1.92265836276
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.9226583627580767, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.92)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
1.30535507398
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 1.02966159834
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.029661598341025, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.03)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
1.19269609991
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 1.89113488921
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 1.8911348892104867, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.89)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', 'right')
2.32349146953
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 2.71339746785
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.7133974678519133, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.71)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.91229039697
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 1.4481102017
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 1.4481102017010734, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.45)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: 2.07980565388
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 2.079805653881676, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.08)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
2.28820794419
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: left, reward: 1.49992606239
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 1.499926062392376, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.50)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: -4.29362070664
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': None, 'reward': -4.293620706637555, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.29)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: left, reward: 0.546247243968
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 22, 't': 8, 'action': 'left', 'reward': 0.5462472439675675, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 0.55)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'right', None)
1.82399470722
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 2.47066455811
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.4706645581065914, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.47)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
1.54191549456
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: 2.73962212165
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 2.7396221216501266, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.74)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
1.60283941415
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 1.13612142184
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 1.1361214218387174, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.14)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: -4.98263993593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 18, 't': 12, 'action': None, 'reward': -4.982639935934522, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.98)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'forward')
2.33841918226
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 0.943200043609
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 17, 't': 13, 'action': None, 'reward': 0.9432000436086445, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.94)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
1.64615766419
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 0.792939689118
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 0.7929396891181206, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.79)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
1.21686622097
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 1.92801572084
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.9280157208405502, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.93)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 2.66584508663
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 14, 't': 16, 'action': None, 'reward': 2.6658450866273284, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.67)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', 'right')
0.185215418233
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: 0.0914049146364
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 0.09140491463644596, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 0.09)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: -9.43409125006
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': -9.434091250061092, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.43)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', None)
1.5724409709
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 1.34878814467
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 11, 't': 19, 'action': None, 'reward': 1.3487881446662677, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.35)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: -9.13368396931
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 20, 'action': 'forward', 'reward': -9.133683969309828, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.13)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'left', None)
1.98030206699
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: left, reward: 1.97174022025
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 9, 't': 21, 'action': 'left', 'reward': 1.9717402202541652, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.97)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, 'left')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: left, reward: 1.13022563458
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 8, 't': 22, 'action': 'left', 'reward': 1.1302256345751145, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.13)
23% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 256
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (5, 3), deadline = 25
0.378023090216
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3780; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', None)
1.56158171438
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.03990727839
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.0399072783948298, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.04)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.17452407925
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.174524079249717, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.17)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.46061455779
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.34057773338
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.3405777333756026, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.34)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 1.61198601403
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.6119860140281121, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.61)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
1.7562910798
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 1.13245532638
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.132455326383688, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.13)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
1.44437320309
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.63347693783
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.633476937829165, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.63)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 0.958912888074
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 0.9589128880742969, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent drove forward instead of left. (rewarded 0.96)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.31553215165
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 2.71609549414
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 2.7160954941443265, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.72)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
1.90708481107
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.13229184372
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.1322918437213247, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.13)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'right')
1.64440127895
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 1.06641467152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.0664146715180705, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.07)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
2.0158138229
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 1.45218798292
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.4521879829238848, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.45)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'right', None)
1.7744537879
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.24658839917
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.246588399172412, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.25)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'right', 'right')
0.745678486432
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 0.358758405029
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 0.35875840502867784, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent drove right instead of forward. (rewarded 0.36)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'left')
1.5832579824
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: 1.06549198787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 1.0654919878745137, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.07)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 0.908216050453
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 14, 'action': None, 'reward': 0.9082160504531913, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.91)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
1.28808853749
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.46798727331
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.4679872733096542, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.47)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
1.3780379054
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.36633754902
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.3663375490188814, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.37)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
1.88000297661
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.5316829096
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.5316829095991935, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.53)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
1.21954867665
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.49108619072
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.4910861907248734, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.49)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
1.35531743369
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.67572995943
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.67572995942514, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.68)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', None)
1.60817948421
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 2.33758371788
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 2.3375837178838053, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.34)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: -5.12608216474
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 4, 't': 21, 'action': None, 'reward': -5.126082164736741, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.13)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, 'left')
1.32437498514
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: left, reward: 2.14559710729
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 2.14559710729049, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.15)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'forward', None)
2.14370386536
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 0.982420127526
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.9824201275261524, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.98)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'forward', 'left')
2.30071520487
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 0.716492619755
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 1, 't': 24, 'action': None, 'reward': 0.7164926197550594, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 0.72)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 257
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (8, 7), deadline = 20
0.376589328346
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3766; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward')
2.61642264785
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.04811131172
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.0481113117229746, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.05)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: -39.6807738077
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -39.680773807704284, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.68)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
1.56306199644
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.1495733201
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.1495733201045846, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.15)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.46870449357
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 0.0974704015674
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.09747040156743225, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.10)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
1.89406700329
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: 1.24518227986
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 1.245182279857213, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.25)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'right', 'forward')
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: -40.8397049884
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -40.83970498837975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.84)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 2.66303850777
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.6630385077682304, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.66)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'right', 'right')
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 0.989973801991
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 13, 't': 7, 'action': None, 'reward': 0.989973801990504, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 0.99)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: 0.43523987199
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 0.43523987198985326, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded 0.44)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.56962464157
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: 2.18737600968
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 2.1873760096789363, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.19)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None)
1.46685459026
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 2.44672566186
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 2.4467256618616733, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.45)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: -5.16723698603
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': None, 'reward': -5.167236986031937, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.17)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'forward')
1.29389388145
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: 1.19982961145
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 1.1998296114492593, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.20)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
2.14076880811
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 1.08078946267
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.0807894626719996, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.08)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, 'forward')
2.33999787648
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.34766179147
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.3476617914738926, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.35)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.61077913539
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 1.05219272645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.0521927264549369, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.05)
20% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 258
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (2, 4), deadline = 25
0.375161004433
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3752; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: left, reward: -40.6150871004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -40.61508710038524, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.62)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.51552369656
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 2.27252588001
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.2725258800132897, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.27)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
1.97288160105
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 1.21216473368
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.2121647336829569, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.21)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
1.59252316736
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 2.58780487702
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.587804877017356, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.59)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', 'right')
1.89557763307
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 2.93031532649
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.9303153264891066, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.93)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
2.09016402219
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 1.26285548606
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.262855486060051, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.26)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: left, reward: -20.4064643742
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -20.406464374199693, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.41)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 1.01107761726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.0110776172647689, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.01)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'forward', None)
0.455581294662
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 1.5034813089
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.5034813088961765, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.50)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None)
1.33148593092
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: right, reward: 1.62268940379
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.6226894037908477, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.62)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.73400090291
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: 1.37307771105
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.3730777110483883, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.37)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: -10.2224504726
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': -10.222450472608017, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.22)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: left, reward: -9.95465968633
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -9.95465968633402, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.95)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
1.79190578575
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 1.27041745286
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.2704174528565162, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.27)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'left')
2.0196883274
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 1.38014296563
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.380142965633393, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.38)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
2.01761311749
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 0.892283314977
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 0.892283314976716, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.89)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
1.45494821623
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 1.61236939731
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.6123693973133018, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.61)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
1.55353930698
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 2.39593058939
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 2.395930589385438, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.40)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', None)
2.08538228637
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: right, reward: 2.44705932641
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 2.447059326412935, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.45)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: None, reward: 1.24873679055
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.2487367905467286, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.25)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: None, reward: 1.57030936956
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.5703093695617145, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.57)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
1.97473494818
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: forward, reward: 0.480750637535
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.4807506375354711, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.48)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'right', None)
1.74879995007
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: forward, reward: 1.0175682386
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 1.0175682386002383, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.02)
8% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 259
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (7, 4), deadline = 20
0.373738097851
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3737; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'right', 'left')
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 2.21511834749
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.2151183474924343, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left')
Agent followed the waypoint right. (rewarded 2.22)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.22774279286
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 2.68290584261
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 2.6829058426096895, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.68)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'left')
1.73498604621
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: left, reward: 2.45800858367
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': 2.458008583668709, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.46)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.39119779866
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 2.29362924672
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.293629246719913, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.29)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.95532431773
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: forward, reward: 2.61649884968
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.616498849680432, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.62)
75% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 260
\-------------------------
Environment.reset(): Trial set up with start = (1, 6), destination = (6, 3), deadline = 30
0.372320588053
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3723; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: forward, reward: -10.781870702
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': -10.781870702040909, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.78)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'left')
1.78138394919
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.79606644984
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.7960664498404446, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.80)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: -9.77050799084
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 28, 't': 2, 'action': 'left', 'reward': -9.770507990835876, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.77)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
1.47708766736
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: right, reward: 1.99128723335
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 27, 't': 3, 'action': 'right', 'reward': 1.9912872333543759, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.99)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 0.952487773409
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 0.9524877734090765, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 0.95)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'right')
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 1.7215069208
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.721506920800447, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.72)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left')
1.64653195819
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 1.55847528782
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 24, 't': 6, 'action': None, 'reward': 1.5584752878175396, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.56)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: 0.888750576801
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 0.8887505768005161, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.89)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
2.03892507046
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: 2.90146046027
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.9014604602727543, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.90)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None)
2.47019276537
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: 2.51380077927
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.5138007792733443, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.51)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
2.49199677232
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: None, reward: 1.19996058586
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.199960585857968, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.20)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
1.97602114362
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: left, reward: 2.47215220103
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': 2.472152201029077, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.47)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, 'forward')
2.00573221182
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 2.17720646535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 12, 'action': None, 'reward': 2.1772064653499132, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.18)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
2.28591158371
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: 0.896324077604
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 0.8963240776035821, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.90)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'right', None)
2.14732963266
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: None, reward: 2.16702599265
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 16, 't': 14, 'action': None, 'reward': 2.167025992650433, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.17)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'right')
1.45059150391
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 0.954757298325
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 15, 't': 15, 'action': 'right', 'reward': 0.9547572983251194, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 0.95)
47% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 261
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (6, 3), deadline = 20
0.370908454571
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3709; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', None)
0.979531301779
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: 0.924690106372
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 0.9246901063716064, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.92)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', 'left')
1.61475030639
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: right, reward: 2.05107104674
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.051071046744255, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.05)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: -4.73780743069
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': -4.73780743068568, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.74)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: 1.4392571311
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.4392571311019862, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.44)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
1.36948041799
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: 1.67251146547
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.6725114654677258, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.67)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', 'left')
1.89559960368
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.4942772615
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.4942772615035975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.49)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', 'right')
2.30257189715
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 1.42649168836
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.4264916883618781, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 1.43)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', None)
1.84597867909
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 0.956488500709
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 0.9564885007085395, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.96)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: -9.90082188391
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.900821883906936, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.90)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', 'right')
0.138310166435
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 1.52147064871
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.521470648707833, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 1.52)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: -4.42345392977
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': -4.423453929770471, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.42)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
1.89402478829
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: 1.5762079198
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.5762079197960122, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.58)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, 'right')
1.41615352665
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: 2.342692629
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.342692628995123, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.34)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'forward')
1.64964829074
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: left, reward: 1.90725156666
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.907251566658555, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.91)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
1.87850032563
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: left, reward: 2.38409573812
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 2.3840957381187584, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.38)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 262
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (1, 5), deadline = 20
0.369501677014
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3695; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', 'forward')
1.41947528926
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: 2.96048277951
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 2.960482779505962, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 2.96)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
1.55069997702
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 2.95352267798
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.9535226779846435, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.95)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
2.2521113275
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.781510252
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.7815102519953996, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.78)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.01681078975
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 2.85955180842
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.8595518084247624, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.86)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.7058429431
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 2.84150592637
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.8415059263707425, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.84)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'left')
1.602503623
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 1.20397363747
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.2039736374651575, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.20)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left')
1.40323863023
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 2.4458346069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.445834606903637, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.45)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: -20.5442748546
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': -20.544274854592807, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.54)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: -9.2663167596
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.266316759595545, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.27)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 0.855188995235
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.8551889952351033, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.86)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: left, reward: -10.4149360308
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -10.414936030841897, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.41)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
1.52099594173
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 2.56116523424
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.5611652342413946, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.56)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'right', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: -9.37546932701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -9.375469327007844, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.38)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: -10.4834345978
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -10.48343459779911, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.48)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.63898219315
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.6389821931500779, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.64)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.73418745036
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 1.10810376991
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.10810376991089, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.11)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', 'forward')
1.3937057543
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.09629299707
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': 2.0962929970679345, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.10)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'forward', None)
2.26622080639
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.00492004087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 1.0049200408671717, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.00)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
2.13129803187
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: left, reward: 1.44676974747
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': 1.4467697474729884, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.45)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'forward', None)
1.35631765827
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 1.13056986352
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.1305698635214712, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.13)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 263
\-------------------------
Environment.reset(): Trial set up with start = (2, 6), destination = (6, 5), deadline = 25
0.368100235068
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3681; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'forward')
0.141564018137
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: right, reward: 0.365868206439
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.3658682064393015, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent drove right instead of left. (rewarded 0.37)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', 'right')
1.60352319606
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 2.64859652877
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 2.648596528774295, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.65)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'forward', 'forward')
1.98442709295
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 2.09113135827
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 2.091131358273478, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 2.09)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.43818129909
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.92754132976
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.9275413297569477, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.93)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
2.27367443474
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 1.26620478473
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.266204784733745, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.27)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
1.84241352269
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.32266724641
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.322667246413307, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.32)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: -10.4480751558
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -10.448075155828226, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.45)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.58254038455
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.76806991286
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.7680699128647217, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.77)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.67530514871
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.69599934015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.695999340150692, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.70)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
1.59111783066
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 2.37182220207
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.371822202073063, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.37)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'right', 'forward')
0.827706059664
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: right, reward: 1.64482931004
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.6448293100438538, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent followed the waypoint right. (rewarded 1.64)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', 'forward')
1.48174000159
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: forward, reward: 2.01667950171
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 2.016679501709831, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.02)
52% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 264
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (2, 6), deadline = 30
0.366704108495
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3667; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', None)
1.4012335899
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: None, reward: 2.95689404787
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 30, 't': 0, 'action': None, 'reward': 2.9568940478706756, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.96)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: -10.1600163779
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': -10.160016377947654, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.16)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 0.721363452158
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.7213634521577269, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.72)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', 'right', 'forward')
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: forward, reward: 1.40845020816
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 1.4084502081582397, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent drove forward instead of right. (rewarded 1.41)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: -4.88065245138
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 26, 't': 4, 'action': None, 'reward': -4.880652451377375, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.88)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, 'forward')
2.34382983398
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 2.60631315655
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 25, 't': 5, 'action': None, 'reward': 2.6063131565476185, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.61)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'left')
2.28872519951
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 2.60199567313
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.601995673134316, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.60)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: right, reward: 1.41772051211
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 1.4177205121117449, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.42)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: right, reward: 0.942278326696
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 0.9422783266963516, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.94)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'right', 'left')
0.703015281436
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: forward, reward: -0.0151290249362
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': -0.015129024936213709, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove forward instead of left. (rewarded -0.02)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
1.67650975413
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: 1.11619431307
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.1161943130711451, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.12)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', 'right')
0.810991257552
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: forward, reward: 1.01593633276
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.015936332764038, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove forward instead of left. (rewarded 1.02)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'right', None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: -5.48779834969
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 18, 't': 12, 'action': None, 'reward': -5.48779834968857, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.49)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: left, reward: -10.6723971464
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 13, 'action': 'left', 'reward': -10.672397146428299, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.67)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
1.73511635404
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.38053193045
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.3805319304516896, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.38)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'left')
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: left, reward: -10.2917315557
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 15, 't': 15, 'action': 'left', 'reward': -10.29173155573875, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.29)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
2.17906381888
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.36372174053
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.3637217405276527, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.36)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
2.22408667233
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: 1.03147312183
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 13, 't': 17, 'action': 'left', 'reward': 1.0314731218301036, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.03)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'left')
1.3837142786
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: 2.3385549986
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': 2.3385549986043213, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.34)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
2.18565224443
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: 1.44341330531
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 19, 'action': None, 'reward': 1.4434133053135703, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.44)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
1.81453277487
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: 1.48972567914
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 20, 'action': None, 'reward': 1.4897256791356976, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.49)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: right, reward: 1.24492595705
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 1.2449259570547828, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.24)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'red', None, None)
1.55782414225
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 1.96594523467
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 22, 'action': None, 'reward': 1.965945234673255, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.97)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', None, None)
1.76188468846
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 0.550793830998
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 23, 'action': None, 'reward': 0.5507938309983569, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.55)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'forward', None)
1.3963520336
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 2.07113074063
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 6, 't': 24, 'action': None, 'reward': 2.0711307406283606, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.07)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: -9.54727254267
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 25, 'action': 'left', 'reward': -9.547272542669903, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.55)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', 'left', None)
1.62777989708
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: left, reward: 1.1927592738
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 26, 'action': 'left', 'reward': 1.19275927379541, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.19)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 265
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (4, 5), deadline = 25
0.365313277136
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
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epsilon = 0.3653; alpha = 0.5000
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epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
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epsilon = 0.3653; alpha = 0.5000
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epsilon = 0.3653; alpha = 0.5000
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epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
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epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3653; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'left')
1.92453661857
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: None, reward: 1.0286479592
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.0286479591997202, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.03)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: right, reward: 0.472573755122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 0.472573755121523, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded 0.47)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.652129227
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 2.82649295952
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.826492959522689, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.83)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'forward', None)
1.5311616193
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: 1.82397395788
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 1.8239739578797256, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.82)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
2.68286131442
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: None, reward: 2.57805622108
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.5780562210838127, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.58)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.76993960974
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: forward, reward: 1.71818619161
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.718186191611469, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.72)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
2.63045876775
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 1.06304526122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.063045261217229, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.06)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
1.74406290067
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 1.00736653381
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.0073665338136195, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.01)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'right', 'left')
0.34394312825
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: 1.00141329938
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.0014132993780942, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove forward instead of left. (rewarded 1.00)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -20.4073363791
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -20.407336379101004, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.41)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
1.78903388967
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: left, reward: 1.31275698295
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 1.3127569829508456, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.31)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: forward, reward: 0.373615810635
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 0.3736158106346251, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.37)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: left, reward: -9.80978678144
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -9.80978678144172, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.81)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: right, reward: -0.0926611003189
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': -0.0926611003189457, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded -0.09)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
2.04108058799
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: right, reward: 1.2719132935
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.27191329349988, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.27)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'left')
2.06400081958
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 0.891329603391
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 0.8913296033910063, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 0.89)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', None)
1.67756778859
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: forward, reward: 1.30206708004
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 1.3020670800388252, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.30)
32% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 266
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (6, 2), deadline = 35
0.363927720907
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
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epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
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epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3639; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'right')
2.41294647978
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: 2.45290367488
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 35, 't': 0, 'action': None, 'reward': 2.4529036748752677, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.45)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: left, reward: -40.662554033
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 34, 't': 1, 'action': 'left', 'reward': -40.662554033003104, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.66)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.77139277971
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: 2.25831477901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 33, 't': 2, 'action': None, 'reward': 2.2583147790117586, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.26)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
2.01485377936
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: 1.12185952312
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 32, 't': 3, 'action': None, 'reward': 1.121859523124339, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.12)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 1.53883573268
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 31, 't': 4, 'action': 'right', 'reward': 1.5388357326809305, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent drove right instead of left. (rewarded 1.54)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: -0.035041434229
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 30, 't': 5, 'action': 'right', 'reward': -0.03504143422896022, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded -0.04)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.55089543631
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: left, reward: 1.49357947055
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 29, 't': 6, 'action': 'left', 'reward': 1.4935794705544776, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.49)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', 'forward')
1.81909497248
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 2.77784790848
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 28, 't': 7, 'action': None, 'reward': 2.777847908478769, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.78)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'left')
1.69493843259
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 2.56286831984
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 27, 't': 8, 'action': None, 'reward': 2.56286831983586, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.56)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: forward, reward: 1.9861670835
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 26, 't': 9, 'action': 'forward', 'reward': 1.9861670834952858, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.99)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'forward')
1.24686174645
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: 1.70885904508
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 25, 't': 10, 'action': 'left', 'reward': 1.7088590450820322, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.71)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
1.65649694074
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 1.6781754652
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 24, 't': 11, 'action': 'right', 'reward': 1.6781754651961405, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.68)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: 1.58827755272
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 12, 'action': 'left', 'reward': 1.588277552722341, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.59)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 1.79380938411
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 22, 't': 13, 'action': 'right', 'reward': 1.7938093841051312, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded 1.79)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
1.15633925973
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 1.8042447333
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 14, 'action': None, 'reward': 1.8042447333017226, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.80)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'right', None)
0.453568959436
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 0.0271804041452
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 20, 't': 15, 'action': 'right', 'reward': 0.02718040414516254, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.03)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
1.41943306112
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 2.56701830379
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 16, 'action': 'right', 'reward': 2.5670183037925134, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.57)
51% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, 'left')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: -5.93530772401
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 18, 't': 17, 'action': None, 'reward': -5.9353077240078225, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.94)
49% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, 'left')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: -5.4199585562
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 17, 't': 18, 'action': None, 'reward': -5.419958556202403, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.42)
46% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, 'left')
1.47659228888
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 0.746465147643
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 16, 't': 19, 'action': None, 'reward': 0.7464651476433593, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.75)
43% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
1.48029199652
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.986639664
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 20, 'action': None, 'reward': 1.9866396639994797, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.99)
40% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', None, None)
1.73346583026
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 2.49066964485
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 21, 'action': None, 'reward': 2.4906696448465824, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.49)
37% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: left, reward: -20.9953512961
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 13, 't': 22, 'action': 'left', 'reward': -20.99535129606293, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -21.00)
34% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'forward', None)
0.441329391178
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: -0.353229612646
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 12, 't': 23, 'action': 'forward', 'reward': -0.35322961264598507, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded -0.35)
31% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
2.11206773755
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: 2.39169679118
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 24, 'action': None, 'reward': 2.391696791177095, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.39)
29% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: left, reward: -20.1084769247
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 10, 't': 25, 'action': 'left', 'reward': -20.10847692469446, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.11)
26% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', None, 'right')
1.82285634517
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: 0.8109597778
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 9, 't': 26, 'action': 'left', 'reward': 0.8109597778002591, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 0.81)
23% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: -39.4862022487
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 8, 't': 27, 'action': 'left', 'reward': -39.48620224869405, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.49)
20% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('left', 'red', 'left', None)
1.56835665124
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.81113273753
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 7, 't': 28, 'action': None, 'reward': 1.8111327375272355, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.81)
17% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('left', 'green', 'left', None)
1.41026958544
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: 0.87883367262
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 6, 't': 29, 'action': 'left', 'reward': 0.878833672619848, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.88)
14% of time remaining to reach destination.
/-------------------
| Step 30 Results
\-------------------
Environment.step(): t = 30
('right', 'green', None, None)
1.66733620297
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: right, reward: 1.55061255798
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 5, 't': 30, 'action': 'right', 'reward': 1.5506125579809615, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.55)
11% of time remaining to reach destination.
/-------------------
| Step 31 Results
\-------------------
Environment.step(): t = 31
('forward', 'green', None, 'right')
1.88971308981
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 1.69651600255
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 4, 't': 31, 'action': 'forward', 'reward': 1.6965160025522272, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 1.70)
9% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 267
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (2, 2), deadline = 20
0.3625474198
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3625; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: -5.10654272013
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 20, 't': 0, 'action': None, 'reward': -5.106542720126254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.11)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.98147001636
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: 1.24033538005
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 1.2403353800512589, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.24)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'forward')
1.64080961293
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.05861116158
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.058611161579094, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.06)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
2.25188226436
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.44877846193
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.4487784619327047, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.45)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'left')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.35303447246
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.3530344724591736, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.35)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'right', None)
0.24037468179
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 1.47523199472
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.4752319947243324, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.48)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: None, reward: 1.95499021155
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.9549902115526057, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.95)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: forward, reward: -9.26036815808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': -9.260368158080851, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.26)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: right, reward: 1.82488358512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.8248835851216851, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.82)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, 'left')
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: right, reward: 1.65360570743
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.6536057074259272, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded 1.65)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, 'forward')
1.92799648645
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: right, reward: 1.90470506637
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.9047050663726766, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.90)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'forward', None)
1.73374138711
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.28747059297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.287470592969585, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.29)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
2.01060599004
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.4410618447
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.441061844699162, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.44)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', 'forward')
1.28863008382
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: 1.40431241583
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.4043124158284055, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 1.40)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'left')
2.09649731494
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: 0.570806147886
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 0.5708061478857569, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 0.57)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: -9.62456830301
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -9.624568303006262, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.62)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'forward', 'left')
1.11021735037
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: forward, reward: 0.785394016557
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.7853940165566686, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 0.79)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
1.52223745343
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: left, reward: 1.77664006287
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 1.7766400628682217, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.78)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 0.290132975979
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.29013297597947263, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.29)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
1.99322568246
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: right, reward: 0.585355127722
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 0.5853551277219493, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.59)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 268
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (1, 3), deadline = 30
0.361172353885
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3612; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
1.64943875815
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 1.97008552375
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 1.9700855237512631, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.97)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: -10.8488817023
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': 'left', 'reward': -10.848881702277689, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.85)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.68974469438
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 2.73137414962
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.731374149619474, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.73)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 1.15850543549
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.1585054354866342, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.16)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 0.485712161115
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 0.4857121611145413, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.49)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 1.12139783544
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.1213978354419525, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.12)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.80976214095
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: left, reward: 1.39626574352
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 1.396265743522948, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.40)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', 'forward')
2.03777922561
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 1.12310614974
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.1231061497410526, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 1.12)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 0.47529736125
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 0.4752973612503443, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.48)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 0.531746815379
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 0.5317468153791405, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent drove right instead of left. (rewarded 0.53)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'forward')
2.47507149526
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.72218691779
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.7221869177917806, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.72)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
1.28929040509
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 2.33748495001
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 2.3374849500118855, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.34)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'right', None)
2.20083726677
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 2.71116665933
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 2.7111666593288444, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 2.71)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 0.634641344155
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 0.6346413441549474, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.63)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 2.10258800225
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 2.1025880022450787, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.10)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 1.23757201581
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.2375720158079717, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.24)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 0.0143377434866
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 0.014337743486635635, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.01)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, None)
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 2.19631471348
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 17, 'action': None, 'reward': 2.1963147134834182, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.20)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'forward')
2.09862920653
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 2.59273586751
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 12, 't': 18, 'action': None, 'reward': 2.592735867506323, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.59)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: left, reward: 1.08708236161
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 19, 'action': 'left', 'reward': 1.0870823616125511, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.09)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: left, reward: -10.8847778511
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 20, 'action': 'left', 'reward': -10.884777851086476, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.88)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, None)
1.60301394224
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 1.96944558543
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 21, 'action': 'left', 'reward': 1.9694455854314865, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.97)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
1.61090269821
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 1.19998370313
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 22, 'action': 'forward', 'reward': 1.1999837031349754, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.20)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: -40.6488383443
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 7, 't': 23, 'action': 'left', 'reward': -40.64883834428419, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.65)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
1.9764591827
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 1.46788652674
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 24, 'action': None, 'reward': 1.467886526735558, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.47)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'green', None, 'left')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: -0.14055361248
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 5, 't': 25, 'action': 'right', 'reward': -0.14055361248000842, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded -0.14)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'red', None, None)
1.81338767755
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 1.66035671399
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 26, 'action': 'right', 'reward': 1.6603567139949729, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.66)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 0.633222118475
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 27, 'action': 'right', 'reward': 0.633222118475262, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.63)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('right', 'green', None, None)
1.12109824948
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: right, reward: 0.84992379057
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 28, 'action': 'right', 'reward': 0.8499237905703019, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.85)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: -39.9571310091
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 1, 't': 29, 'action': 'forward', 'reward': -39.957131009092194, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.96)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 269
\-------------------------
Environment.reset(): Trial set up with start = (2, 6), destination = (6, 6), deadline = 20
0.359802503305
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3598; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.72217285472
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 1.21128216613
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.2112821661303093, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.21)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.46672751042
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 1.88958628638
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.889586286379054, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.89)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.6781568984
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 1.3277514821
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.327751482100926, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.33)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, None)
1.78622976383
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: 1.04934101577
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.0493410157729417, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.05)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'forward')
1.47786039577
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: 1.7284927678
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 1.7284927677997781, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.73)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
1.86406897453
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 2.69953227204
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.6995322720403014, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.70)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
2.09715065241
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 1.70335378613
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.703353786125581, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.70)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.40544320067
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 2.83489027059
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.8348902705934345, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.83)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.27730483821
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.277304838206749, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.28)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: left, reward: 1.51776532202
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.5177653220177265, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.52)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', 'left', 'left')
1.62148905854
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 2.65532519704
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 2.6553251970368787, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.66)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'left')
2.44536043632
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 2.37962130708
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.3796213070790158, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.38)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
1.73687219577
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 2.33041379836
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 2.330413798363395, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.33)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: forward, reward: -9.34558600272
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -9.345586002720566, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.35)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: None, reward: 1.76978196348
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.7697819634835, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.77)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: right, reward: 0.317114567382
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.3171145673823943, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent drove right instead of forward. (rewarded 0.32)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
2.210559422
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: None, reward: 1.34849338496
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.3484933849606153, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.35)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
1.14455162903
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: left, reward: 0.834392408584
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 0.8343924085839725, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.83)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', 'left', None)
0.989472018806
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: 0.509387928346
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': 0.509387928345731, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.51)
5% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 270
\-------------------------
Environment.reset(): Trial set up with start = (1, 3), destination = (4, 4), deadline = 20
0.358437848279
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3584; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: right, reward: 0.542892456579
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 0.5428924565785647, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent drove right instead of forward. (rewarded 0.54)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'forward', None)
0.429724801878
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 1.28813629564
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.2881362956414795, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.29)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: 2.75638353862
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.7563835386154762, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.76)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
1.95679012606
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 2.66906010727
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.669060107267893, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.67)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.8522672676
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.8522672675990908, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.85)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
1.67928024611
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.07206036925
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.072060369252137, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
1.87567030768
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 1.47636695817
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.4763669581744145, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.48)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
2.12016673563
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 1.24363746665
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.2436374666475403, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.24)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', 'right')
0.744109768389
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.12604363669
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.1260436366858275, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 1.13)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.67601863293
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 0.862641066078
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 0.8626410660780912, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.86)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'right')
1.35540797524
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.02702953121
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.0270295312058986, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.03)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.68190210114
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 2.55372655503
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 2.553726555030635, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.55)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
1.2693298495
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: None, reward: 2.15418014681
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.1541801468073336, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.15)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
2.11781432809
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: 2.51805666752
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 2.518056667522194, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.52)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: -9.10069343481
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -9.1006934348132, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.10)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'right', None)
2.15717781266
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 0.818464189132
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.8184641891321434, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.82)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, None)
2.03364299707
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: right, reward: 1.94694429776
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.9469442977577036, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.95)
15% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 271
\-------------------------
Environment.reset(): Trial set up with start = (1, 5), destination = (4, 7), deadline = 25
0.357078369102
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3571; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.99029364741
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: right, reward: 2.14527304322
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 2.145273043217385, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.15)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: forward, reward: 2.56630900474
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 2.5663090047411963, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.57)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: left, reward: 0.199759946126
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': 0.1997599461258368, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.20)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
2.31292511667
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: right, reward: 1.43961895064
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.4396189506445183, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.44)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: left, reward: -10.7910558375
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -10.791055837456373, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.79)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
0.966067349561
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 1.66776983842
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.667769838422549, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.67)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', 'left')
0.947805683464
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: 0.119211444066
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 0.11921144406618367, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 0.12)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'forward')
1.54364120153
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 2.71664695236
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.716646952362792, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.72)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.50295419025
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 1.01235708453
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.01235708453252, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.01)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: -5.9073639324
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': -5.907363932400803, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.91)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
1.4177853898
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: 2.13583232977
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 2.1358323297676876, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.14)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: -10.7322044409
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -10.732204440878279, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.73)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
1.25765563739
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 2.07752543474
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.0775254347365264, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.08)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'forward', None)
1.72583391737
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.14649338504
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 12, 't': 13, 'action': None, 'reward': 1.1464933850446806, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.15)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', 'left')
1.34159114505
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 2.48957897366
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 11, 't': 14, 'action': None, 'reward': 2.4895789736598433, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.49)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: -9.58453324848
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': -9.584533248482618, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.58)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', 'forward')
0.97655507766
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: left, reward: 1.17573132072
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 1.1757313207246618, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.18)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: right, reward: 1.75873810703
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 1.7587381070320647, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.76)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: None, reward: -5.32703194994
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': -5.3270319499381475, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.33)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, None)
1.71175499816
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: None, reward: 0.894187257077
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': 0.894187257076976, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.89)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: right, reward: -20.9259957383
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': -20.92599573832234, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.93)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', None, None)
1.30297112762
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: None, reward: 0.972899848996
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 4, 't': 21, 'action': None, 'reward': 0.9728998489956464, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.97)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'left', 'left')
1.54188897776
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: 0.933389989271
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.9333899892713213, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 0.93)
8% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 272
\-------------------------
Environment.reset(): Trial set up with start = (2, 6), destination = (8, 3), deadline = 25
0.355724046143
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
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epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
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epsilon = 0.3557; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3557; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', None)
1.87627203365
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: right, reward: 2.64249364343
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 2.6424936434277484, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.64)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
2.44212225127
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 1.91468974297
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.9146897429712342, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.91)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 0.206378993539
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.20637899353935074, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.21)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
1.8611346386
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: forward, reward: 2.02111023993
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 2.021110239933652, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.02)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'right', 'left')
1.93510160881
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: forward, reward: 0.969651151672
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 0.9696511516722881, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 0.97)
80% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 273
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (2, 5), deadline = 20
0.354374859845
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3544; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', 'left')
0.638574708882
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: 0.324460994921
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 0.3244609949210011, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded 0.32)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
1.07443641895
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: 0.416804714709
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 0.41680471470881664, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.42)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: -40.1610221276
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -40.161022127630716, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.16)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
1.66759053606
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 1.92554288019
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.925542880187966, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.93)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
1.79656670813
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 1.52375857517
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.5237585751698435, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.52)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'left')
1.33365173141
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: left, reward: 1.9670336433
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.9670336432988595, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.97)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
2.17840599712
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 2.49266089471
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.492660894709645, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.49)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'left', 'right')
1.58713024748
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 2.29963024536
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 2.2996302453628523, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.30)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'left')
1.94112243927
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: forward, reward: 2.36434545398
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 2.364345453975046, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.36)
55% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 274
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (7, 6), deadline = 30
0.353030790726
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3530; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'right')
2.12605986242
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 2.69986561194
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 2.6998656119354045, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.70)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.13671950884
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.136719508837926, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.14)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.49173576166
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 2.48848542231
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.4884854223076562, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.49)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.99011059198
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.53968940612
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.5396894061207023, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.54)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 1.19946112413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 1.1994611241346833, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.20)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: -0.0343463056159
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 25, 't': 5, 'action': 'right', 'reward': -0.03434630561591134, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded -0.03)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: forward, reward: -39.3676650344
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': -39.367665034411715, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.37)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 2.2509057218
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 23, 't': 7, 'action': None, 'reward': 2.250905721795975, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.25)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'left')
1.99159365858
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 2.0238723097
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.0238723097022384, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.02)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
1.43616365121
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 2.29188781358
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.291887813579775, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.29)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
1.86402573239
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.93627473633
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.9362747363334993, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.94)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: 1.65656665442
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.6565666544198803, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.66)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 0.78786269654
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 0.7878626965397891, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.79)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
1.66016264165
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 2.32015472336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 13, 'action': None, 'reward': 2.320154723356925, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.32)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', None)
1.80074449639
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 0.866668336948
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 16, 't': 14, 'action': None, 'reward': 0.8666683369481236, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.87)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
1.77952640348
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 2.66906820003
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 15, 'action': None, 'reward': 2.6690682000281325, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.67)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', 'left')
1.91558505935
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 2.54614364122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 14, 't': 16, 'action': None, 'reward': 2.5461436412179363, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.55)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', 'right')
0.829890407571
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 0.54006821737
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 0.5400682173695497, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 0.54)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
1.77680885979
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: left, reward: 1.74397534014
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 18, 'action': 'left', 'reward': 1.7439753401401028, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.74)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', 'left')
1.90704859967
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: 2.19083102485
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 11, 't': 19, 'action': 'left', 'reward': 2.1908310248528124, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 2.19)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: 1.56717696163
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 10, 't': 20, 'action': 'left', 'reward': 1.567176961627346, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.57)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', 'forward', None)
1.57432598418
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 2.20436705575
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 2.2043670557455606, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.20)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', 'left', 'forward')
1.74920975165
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 0.622270928442
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 8, 't': 22, 'action': 'forward', 'reward': 0.6222709284416639, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 0.62)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', 'left', None)
2.25938283854
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 1.54341783999
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 7, 't': 23, 'action': 'right', 'reward': 1.5434178399943543, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.54)
20% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 275
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (8, 6), deadline = 30
0.351691819378
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3517; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'forward')
1.91635077641
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 1.32791222703
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.3279122270252297, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.33)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
1.13793548831
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 2.86874192372
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.8687419237249516, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.87)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.74498411607
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.7449841160702526, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.74)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'left')
2.27609145706
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.49396488649
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.4939648864852246, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.49)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
2.33553344592
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 2.59161189987
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 2.5916118998734468, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.59)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.44020101225
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: 1.06312830328
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 1.0631283032782948, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.06)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
1.48981743432
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: 2.50450232943
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 2.5045023294275337, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.50)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 0.846661587734
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 23, 't': 7, 'action': None, 'reward': 0.8466615877335261, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded 0.85)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', 'left', 'forward')
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 1.7956019681
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 1.795601968103022, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.80)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.76489999905
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.54780538738
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 9, 'action': None, 'reward': 1.54780538738318, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.55)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
1.25166465777
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: forward, reward: 1.99027923316
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 1.9902792331556984, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.99)
63% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 276
\-------------------------
Environment.reset(): Trial set up with start = (6, 2), destination = (5, 5), deadline = 20
0.350357926466
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3504; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: left, reward: 1.99380645435
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.993806454354222, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 1.99)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
1.90140033927
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: right, reward: 1.54987645138
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.5498764513816856, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.55)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: -10.2843348747
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.284334874675029, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -10.28)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', 'forward')
2.20887865775
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.41974102819
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.4197410281851712, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.42)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
1.90015023436
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.86401088862
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.8640108886220257, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.86)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
1.88208056149
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 0.963134146844
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 0.963134146843827, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.96)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', 'forward')
1.34647124982
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: forward, reward: 1.08891923225
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.0889192322501366, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 1.09)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.76039209996
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: left, reward: 2.33493718007
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.334937180065573, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.33)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'left', None)
1.31691859399
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: None, reward: 0.25869825767
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.2586982576696851, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.26)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 0.910231876641
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.9102318766414024, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.91)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward')
2.13014407695
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 2.42941217718
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.4294121771787567, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.43)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -10.0298826607
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -10.029882660672685, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.03)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
2.22429730175
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 1.90834745517
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.9083474551742206, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.91)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 0.971067369694
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.9710673696938107, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.97)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'right', None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -19.7734378535
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': -19.773437853487223, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.77)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: left, reward: 1.56785040553
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 1.5678504055324591, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.57)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'forward')
2.27977812706
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.37728570155
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.3772857015456277, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.38)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', 'forward')
2.20766179667
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.86396367919
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.8639636791880185, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.86)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
2.06632237846
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.69571561731
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.6957156173113266, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.70)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 2.08212913978
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 1, 't': 19, 'action': None, 'reward': 2.0821291397762867, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.08)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 277
\-------------------------
Environment.reset(): Trial set up with start = (5, 2), destination = (2, 4), deadline = 25
0.349029092729
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
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epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3490; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'right')
0.461076399034
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 0.159421053184
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.15942105318390687, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent drove right instead of forward. (rewarded 0.16)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
2.04766464001
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: left, reward: 2.82051406093
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 2.820514060930889, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.82)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: 0.900652970363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.9006529703633837, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.90)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'right', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 2.59574194499
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.595741944992561, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.60)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
1.9901586825
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 2.59079311069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.590793110687329, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.59)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'right', None)
0.857803338257
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 1.35701519588
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.3570151958776493, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.36)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: -4.89351571757
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': -4.8935157175736945, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.89)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
2.43408935047
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: 2.03789288282
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 2.037892882820394, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.04)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
1.42260735417
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: None, reward: 2.18205359575
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.1820535957537444, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.18)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: None, reward: -5.58827546515
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': None, 'reward': -5.588275465147896, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.59)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'left', None)
1.15864018955
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: left, reward: 2.81840878494
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 2.8184087849434194, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.82)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
1.87416141104
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: None, reward: 2.62832743743
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.62832743743473, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.63)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: left, reward: -10.4983850173
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -10.498385017252602, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.50)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
2.46357267289
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: forward, reward: 2.52752535082
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 2.527525350824792, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.53)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: 1.27576613933
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 1.2757661393270348, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.28)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'right', 'forward')
1.23626768485
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 2.17833763228
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 2.1783376322756656, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent followed the waypoint right. (rewarded 2.18)
36% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 278
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (4, 7), deadline = 25
0.347705298977
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3477; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', 'left')
0.481517851902
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: 1.32575115386
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 1.3257511538603042, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded 1.33)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: -9.56262179638
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -9.5626217963815, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.56)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
1.63557042363
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.25990203768
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.2599020376798662, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.26)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
2.15273394662
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 1.61534173123
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 1.6153417312321645, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.62)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
2.25124442424
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 2.12450707197
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.1245070719685337, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.12)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
2.1878757481
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 2.14729871611
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.1472987161064427, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.15)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
2.49554901186
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 2.84250393625
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 2.8425039362463, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.84)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
2.09146933858
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 1.94586480529
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.945864805292737, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.95)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
2.16758723211
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 1.0493992491
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.0493992491030935, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.05)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.6084932406
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 1.71879679691
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.7187967969133318, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.72)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
2.66902647405
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 2.56975715841
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.56975715841096, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.57)
56% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 279
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (8, 5), deadline = 20
0.346386526097
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
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Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
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epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3464; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'left')
1.80576506123
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: forward, reward: 1.08919519999
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.0891951999854552, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.09)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
1.65635269322
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: None, reward: 1.26857360064
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.2685736006444999, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.27)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'left')
2.12890337621
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: None, reward: 1.93764882781
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.937648827808987, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.94)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: 0.681435911782
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 0.681435911781706, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent drove left instead of forward. (rewarded 0.68)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'left', None)
2.02026936516
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 1.30253080637
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.3025308063710623, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.30)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 1.5529461396
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.5529461396033348, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.55)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'right', None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: 1.39531916071
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.3953191607119526, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.40)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
2.23599111665
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 2.45201794398
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.4520179439837033, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.45)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: -9.16158838772
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': -9.161588387722116, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.16)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: -10.5674118529
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': -10.567411852899243, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.57)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.61709170186
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.6170917018585205, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.62)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', None)
1.66140008576
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: right, reward: 2.11829480172
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.1182948017161594, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.12)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 280
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (8, 4), deadline = 20
0.345072755043
Simulating trial. . .
epsilon = 0.3451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3451; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, 'forward')
1.58517677171
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: left, reward: 1.76078842027
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.7607884202709696, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 1.76)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'left')
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: forward, reward: -9.91814845067
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -9.918148450672296, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.92)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
1.44773623065
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: right, reward: 2.41654871076
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.4165487107645616, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.42)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
1.80233047496
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.39446368545
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.3944636854500863, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.39)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
1.59839708021
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 2.86375476646
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.863754766461783, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.86)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.7576687506
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.7576687505988495, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.76)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: 0.660618421961
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 0.6606184219609892, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.66)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
1.7784499287
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: left, reward: 1.56929900099
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.5692990009879515, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.57)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'right', 'right')
0.0547832679685
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: forward, reward: 1.51420547092
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'right'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.5142054709150217, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'right')
Agent drove forward instead of left. (rewarded 1.51)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
2.34400453032
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: 1.70421618175
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.7042161817517985, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.70)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 281
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (7, 6), deadline = 25
0.343763966847
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3438; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
2.29047589659
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: None, reward: 2.90665687546
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.906656875462308, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.91)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
2.59856638603
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: None, reward: 1.35119419239
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.3511941923911643, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.35)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'right', None)
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: right, reward: 0.894985316224
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.8949853162235126, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent drove right instead of left. (rewarded 0.89)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (0, -1), action: left, reward: 0.00357722547319
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'left', 'reward': 0.0035772254731940567, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.00)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
0.985511020023
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: right, reward: 1.37521573244
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.3752157324418781, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.38)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: -10.9969937091
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -10.996993709134923, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -11.00)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', 'left')
2.03327610201
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 2.81243718597
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.8124371859653525, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.81)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', 'left')
1.23763948351
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: forward, reward: 2.08596477125
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 2.0859647712512372, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.09)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.66364501876
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: None, reward: 2.11049247186
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.110492471862047, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.11)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
2.61939181623
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: forward, reward: 1.76835212227
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.7683521222731882, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.77)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
2.06778334532
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 1.52065365651
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.5206536565097857, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.52)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: -4.24538246688
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 14, 't': 11, 'action': None, 'reward': -4.245382466883193, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.25)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'left')
1.88403783893
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: forward, reward: 1.84847848292
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 1.848478482915967, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.85)
48% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 282
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (2, 5), deadline = 25
0.342460142608
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3425; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'right', 'left')
1.17380750454
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 0.82339625005
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 0.8233962500503753, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent drove forward instead of right. (rewarded 0.82)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'forward')
1.62213150172
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 2.88663597579
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 2.8866359757861537, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.89)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: -4.31283661307
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 23, 't': 2, 'action': None, 'reward': -4.312836613066103, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.31)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, 'forward')
2.25438373875
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 2.90156316641
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 2.9015631664129202, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.90)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.77949854061
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.7794985406136483, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.78)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.62097194546
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 1.10734998217
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.1073499821695745, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.11)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 0.713194369689
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 0.7131943696885431, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.71)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: 2.80789695351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 2.8078969535118237, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.81)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
1.65034268736
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: 1.89059780816
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 1.8905978081604375, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.89)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'left')
1.86625816092
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: 1.85037750941
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.8503775094096513, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.85)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'forward')
2.01866707194
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: None, reward: 1.24130870637
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.2413087063727106, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.24)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
2.19387196925
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: 0.893839520486
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 0.8938395204855218, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.89)
52% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 283
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (2, 3), deadline = 25
0.341161263499
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3412; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'forward')
0.253716112288
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 0.940467042751
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.9404670427509141, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent drove right instead of left. (rewarded 0.94)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
1.67298259599
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: 0.330566892777
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 0.3305668927769445, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.33)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
1.18036337623
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 1.68779354838
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.6877935483759807, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.69)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'forward')
1.62998788916
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 2.58969002303
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.589690023029074, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.59)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: forward, reward: -9.68875561508
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': -9.68875561508281, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.69)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'right')
1.79311454618
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 1.15281185523
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.152811855233326, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 1.15)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
1.36416096382
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 2.54442901872
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 2.5444290187193586, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.54)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.2775038861
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.2775038861042876, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.28)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
1.88502817177
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.52275101782
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.522751017823893, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.52)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
1.54385574487
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 1.82614843963
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.8261484396262697, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.83)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'right', 'right')
1.66278281798
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.77703318781
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'right'), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.777033187806378, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 2.78)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'right', None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.75680475533
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.756804755325042, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.76)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, 'right')
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: -10.4894403872
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': -10.489440387220602, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -10.49)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'right', None)
1.10740926707
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: 0.247334282152
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 0.24733428215248332, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.25)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'right', None)
1.48782100089
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 0.727803938508
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 11, 't': 14, 'action': None, 'reward': 0.7278039385076365, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 0.73)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'right', None)
1.32707754762
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 2.35556992146
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 2.3555699214557118, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 2.36)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
1.4340784623
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 1.24414833506
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 1.2441483350616946, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.24)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', None)
1.72563839532
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: right, reward: 1.78310554612
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 1.7831055461236167, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.78)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'forward', None)
1.99437233697
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.10159540142
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.1015954014219427, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.10)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: left, reward: -9.77850030058
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 6, 't': 19, 'action': 'left', 'reward': -9.77850030057603, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.78)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'forward', None)
1.54798386919
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.95408707155
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.954087071546674, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.95)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'forward', None)
0.85893054876
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: -0.180356876655
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 4, 't': 21, 'action': 'right', 'reward': -0.18035687665504063, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded -0.18)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, None)
1.79421850091
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 1.82753041643
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 1.8275304164323454, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.83)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'left', 'right')
1.94338024642
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 1.49535433831
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.495354338314068, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.50)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', 'left', 'forward')
1.83189067783
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: right, reward: 1.47500123663
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 1.4750012366309777, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.48)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 284
\-------------------------
Environment.reset(): Trial set up with start = (6, 3), destination = (3, 5), deadline = 25
0.339867310765
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3399; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 2.66411875691
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.6641187569091977, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.66)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: left, reward: -39.6191561603
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -39.61915616030126, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.62)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'forward')
1.83226697978
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 1.90172573668
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.9017257366810008, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.90)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: right, reward: 1.72574907082
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.725749070820058, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.73)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', 'forward')
1.81430984297
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 2.35161936295
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.3516193629519293, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.35)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: right, reward: 1.44983602699
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.449836026985071, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.45)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: right, reward: 0.936866154126
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 0.936866154126197, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded 0.94)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
2.02411035603
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: left, reward: 1.61486824138
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 1.6148682413758642, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.61)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
1.2238787903
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 1.42400845029
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.4240084502932062, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.42)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'forward')
1.3239436203
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 1.66091126844
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 1.6609112684427805, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.66)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'forward')
1.49242744437
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 2.25209597709
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.252095977090188, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.25)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'left', 'right')
2.41296273718
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: 1.92017909951
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.920179099505033, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.92)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.68500209225
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: forward, reward: 1.50830124415
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 1.5083012441508792, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.51)
48% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 285
\-------------------------
Environment.reset(): Trial set up with start = (1, 6), destination = (5, 7), deadline = 25
0.338578265721
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3386; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', 'right')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 2.89630353983
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 2.896303539834434, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 2.90)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', 'forward')
1.29821249085
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 1.65804604495
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.6580460449459022, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent drove right instead of forward. (rewarded 1.66)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'right')
1.31690806148
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: left, reward: 1.07429905783
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 23, 't': 2, 'action': 'left', 'reward': 1.0742990578284843, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.07)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.34753125053
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: right, reward: 0.381292695893
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.3812926958932201, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.38)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: right, reward: 0.36825804232
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.36825804232018877, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 0.37)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
1.88101899789
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.07986691636
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.0798669163551486, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.08)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', 'left')
2.04893981226
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: left, reward: 1.16034403107
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 1.16034403107488, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 1.16)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', 'left')
1.60464192167
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: left, reward: 1.80750485116
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 1.8075048511565155, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 1.81)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'forward')
2.10983895609
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 2.50730213475
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.507302134746062, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.51)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
2.37320253631
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 2.14645784387
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.146457843871977, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.15)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'left')
1.85831783517
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 2.40647546753
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.406475467529173, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.41)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
1.95429499127
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: 2.04494532037
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 2.0449453203741887, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.04)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, 'left')
2.11702437505
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 1.21584756944
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.2158475694362438, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.22)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.5966516682
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: 1.17604354569
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.1760435456921534, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.18)
44% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 286
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (5, 7), deadline = 20
0.337294109753
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3373; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: 1.41717932524
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.4171793252415565, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.42)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.97488028921
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.70084594448
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.7008459444821344, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.70)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: -10.908797248
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.90879724797172, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.91)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
1.75103547037
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.55373440201
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.5537344020145019, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.55)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'forward', 'right')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 0.985162903438
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.9851629034378192, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove right instead of left. (rewarded 0.99)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
1.88984744374
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 2.25308921819
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.253089218186366, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.25)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 0.664970612604
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 0.6649706126035202, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.66)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: 1.73069268211
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.7306926821062232, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.73)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'left', None)
1.75437197072
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: 1.0914470901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.0914470900993753, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.09)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: right, reward: -19.279884107
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': -19.279884107025627, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.28)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: -9.53673532574
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': -9.536735325735382, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.54)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.36728865601
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.3672886560115636, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded 1.37)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: -20.9009427535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': -20.90094275350147, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.90)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
2.33786311685
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 0.963564184908
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.9635641849075283, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.96)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
1.65071365088
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.27410910975
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.2741091097453587, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.27)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
1.46241138031
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.33186149062
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.3318614906212454, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.33)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: left, reward: 0.642217297919
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 0.6422172979190457, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.64)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.25403363592
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.2540336359165942, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.25)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 2.18921009377
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 2.189210093766371, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.19)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'forward', 'right')
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: -0.0401239982788
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 1, 't': 19, 'action': 'right', 'reward': -0.040123998278767226, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent drove right instead of forward. (rewarded -0.04)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 287
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (8, 2), deadline = 25
0.336014824317
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3360; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.39713643547
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 1.22042791776
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.2204279177634254, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.22)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.30878217661
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 1.88895187306
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.888951873061253, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.89)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.59886702484
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 2.46485487347
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.464854873470401, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.46)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
2.03186094915
Environment.act() [POST]: location: (6, 5), heading: (0, 1), action: None, reward: 1.87325190049
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.8732519004858827, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.87)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
1.13027580495
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: left, reward: 2.09419790124
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 2.0941979012423815, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.09)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', 'forward')
2.29847144048
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.44625960807
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.4462596080730226, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.45)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 0.300836429136
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 0.30083642913585174, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.30)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'left')
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: forward, reward: -9.08905844813
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': -9.0890584481283, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.09)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: forward, reward: -9.34088719959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -9.340887199588854, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.34)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: -10.2441017584
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -10.244101758396887, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.24)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'forward', None)
0.894561431519
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 1.65845877533
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.6584587753255269, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.66)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'forward')
2.24088570918
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 2.76193758614
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 2.7619375861436266, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.76)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 1.01492964229
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 1.014929642290814, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.01)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
1.61223685309
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: left, reward: 2.54424047817
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 2.5442404781705967, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.54)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', 'forward')
2.37236552428
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 1.16830637073
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.168306370729023, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.17)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
1.46246314693
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 0.903836949722
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 0.9038369497218424, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.90)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
1.99962015582
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 2.05113375208
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 2.0511337520754642, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.05)
32% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 288
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (6, 2), deadline = 25
0.334740390942
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3347; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: None, reward: 1.77587245943
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.7758724594279838, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.78)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'right')
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: None, reward: 1.37804905932
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.3780490593157801, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.38)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, 1), action: left, reward: -9.06859631377
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -9.068596313769914, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.07)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
1.81087445867
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: right, reward: 1.4439834293
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.4439834292996607, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.44)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.50348056244
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.503480562439023, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.50)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
2.02537695395
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 2.41915069148
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.419150691481708, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.42)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', 'left')
1.44748013061
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 1.18794530568
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.187945305684403, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.19)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: -4.49761699907
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': -4.497616999071168, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.50)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
1.87226171073
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 1.85576441415
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.8557644141471084, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.86)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
1.98852448725
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: 1.38594547806
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 1.3859454780583946, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.39)
60% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 289
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (6, 7), deadline = 20
0.333470791224
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3335; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'right', 'left')
0.998601877293
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: forward, reward: 1.83546841921
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.8354684192123967, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent drove forward instead of right. (rewarded 1.84)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
1.33911339868
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 1.14965279797
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.1496527979706905, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.15)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.84331530538
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 2.69634661622
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.6963466162245204, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.70)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.2698309608
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.26954647019
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.2695464701864247, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.27)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
1.7696887155
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 0.962926390012
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 0.9629263900121192, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.96)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: -20.378427169
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': -20.37842716900608, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.38)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
2.22226382271
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: 1.91791379433
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.9179137943267464, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.92)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
2.07008880852
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 1.4077750959
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.4077750958986064, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.41)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
1.38634760695
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 1.97741262149
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.9774126214868404, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.98)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'forward')
2.57797345258
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 1.46797640251
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.4679764025087692, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.47)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 290
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (6, 3), deadline = 20
0.332206006829
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3322; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
1.756931913
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.12607437189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.126074371886071, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.13)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: -9.21767922772
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -9.217679227724847, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.22)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: -39.9235636148
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -39.923563614792336, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.92)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: -0.0386074303986
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': -0.03860743039861714, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent drove right instead of forward. (rewarded -0.04)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
1.95255642482
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.38716909433
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.3871690943314627, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.39)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.79798353492
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.7979835349221913, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.80)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
2.07823866563
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: left, reward: 1.18941678996
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 1.1894167899610264, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.19)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
2.2038895948
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 2.27302423111
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.273024231110074, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.27)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
2.23845691295
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 1.29757084659
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.2975708465883191, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.30)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: right, reward: 0.16623924599
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.16623924598965645, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.17)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
1.65238493619
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: None, reward: 2.03036843964
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.030368439639398, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.03)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'forward', 'right')
2.43292507733
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: None, reward: 2.33583765564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.335837655643311, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.34)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: left, reward: -9.73658449135
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -9.736584491350143, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.74)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: forward, reward: 0.889049909131
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.8890499091308083, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.89)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
1.6338277278
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: left, reward: 1.65163835147
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 1.6516383514664383, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.65)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.62742894399
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: right, reward: 1.52093008587
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.5209300858739736, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.52)
20% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 291
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (8, 2), deadline = 30
0.330946019495
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3309; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'forward')
2.02297492755
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 1.98590455317
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.9859045531704482, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.99)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: -5.91784334432
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 29, 't': 1, 'action': None, 'reward': -5.917843344321197, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.92)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: 0.876497312655
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 28, 't': 2, 'action': 'left', 'reward': 0.8764973126554741, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded 0.88)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 1.07488383804
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.0748838380412566, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.07)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, 'forward')
1.71028318753
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 1.16732440078
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.1673244007799584, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.17)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'right', None)
2.45600196305
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 2.69083928133
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 2.6908392813271247, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 2.69)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: left, reward: 0.0143122927952
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 0.01431229279518631, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.01)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', None)
1.88934651997
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: right, reward: 2.82222024329
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 2.822220243287785, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.82)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'right', 'forward')
0.969708775973
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 2.37839292804
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'forward'), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.37839292803828, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 2.38)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'left')
1.77047024776
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: left, reward: 1.39721876681
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 1.3972187668108467, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.40)
67% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 292
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (1, 3), deadline = 25
0.329690811028
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3297; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: forward, reward: -10.4647183895
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': -10.464718389538815, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.46)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'forward')
2.05897093885
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 1.74060942065
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.7406094206480296, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.74)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: left, reward: -10.7370543266
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -10.73705432664108, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.74)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.48044295712
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 2.41753576279
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.4175357627876766, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.42)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'left', None)
1.68723498265
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: left, reward: 2.42589721536
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 2.4258972153573426, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.43)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: left, reward: 2.26583660902
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 2.2658366090215774, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.27)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 1.36094878124
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.360948781242668, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.36)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: 0.818636869848
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 0.818636869847859, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.82)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
1.58384450728
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: left, reward: 1.01027706245
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 1.0102770624451258, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.01)
64% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 293
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (1, 3), deadline = 20
0.328440363301
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3284; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: forward, reward: 1.5402071606
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.5402071606009067, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 1.54)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: None, reward: 2.89017839582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.8901783958221863, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.89)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
2.20584015636
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: None, reward: 1.17550927048
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.1755092704776329, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.18)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'right', None)
1.84132373454
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 1.41104122394
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.4110412239403107, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.41)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'left')
1.76801387977
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 2.71564222011
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.7156422201092734, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.72)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
1.94150314244
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 2.55106569137
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.55106569137289, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.55)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', None)
1.71632692733
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 2.756847203
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.756847203002444, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.76)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', None)
1.99715988187
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 1.03131283546
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.0313128354553422, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.03)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'forward')
1.7703359475
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 2.51898565558
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.51898565558215, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.52)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
1.73893195221
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 2.05299147496
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 2.0529914749618072, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.05)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 294
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (4, 2), deadline = 20
0.327194658259
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3272; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', None)
2.21841321305
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.69073439702
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.6907343970196471, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.69)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
2.48392314725
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.48868315379
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.488683153789162, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.49)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.98630315052
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 2.49543876263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.495438762625323, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.50)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'right', None)
1.95457380503
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 2.35226771928
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.35226771927662, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.35)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
2.24087095657
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 0.987330676614
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 0.987330676614218, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.99)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
1.94898935995
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 2.33602402659
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.3360240265908345, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.34)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: forward, reward: 0.155387014128
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 0.1553870141279925, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove forward instead of left. (rewarded 0.16)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'right')
1.19560355966
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: left, reward: 2.02600790718
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.0260079071753774, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.03)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', 'left', None)
2.07146833096
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: 1.26549496075
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.265494960753809, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.27)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
2.24628441691
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 1.29169268498
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.2916926849752446, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.29)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: None, reward: 2.70773945547
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.70773945546928, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.71)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
1.89596171359
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: forward, reward: 1.58850881024
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.588508810238866, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.59)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 295
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (5, 2), deadline = 20
0.325953677914
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3260; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', None)
1.84137668792
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 2.44424542824
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.4442454282392303, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.44)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: forward, reward: -10.9809318831
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -10.980931883088717, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -10.98)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', 'left')
1.36485711163
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: None, reward: 1.66847998553
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.6684799855252497, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.67)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'forward', None)
1.27651010342
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 1.79831130519
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.7983113051899275, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.80)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, 'left')
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: -5.21795728728
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': None, 'reward': -5.217957287280474, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.22)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'left', 'left')
1.83291067657
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 2.38373986132
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.383739861319115, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.38)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
1.68188011422
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 1.12260390593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.1226039059283182, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.12)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'right', 'left')
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: left, reward: -40.2703169623
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', 'left'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -40.270316962307504, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.27)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.61410081659
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: 2.35108500116
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.351085001158397, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.35)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: forward, reward: 0.906920669971
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 0.906920669971274, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.91)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: forward, reward: 0.44267761363
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 0.4426776136304723, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.44)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
1.64273303963
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: left, reward: 1.19331722687
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 1.1933172268694767, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.19)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
2.14281105808
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 1.1918403944
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.1918403943970903, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.19)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
1.53741070431
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: right, reward: 0.436658126473
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.4366581264733215, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.44)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: -5.76355572473
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 6, 't': 14, 'action': None, 'reward': -5.76355572472511, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.76)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
1.98259290888
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: 1.71118781297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.7111878129686364, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.71)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: right, reward: 0.930513111428
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 0.9305131114277447, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.93)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'left')
2.09418166556
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: None, reward: 2.15848542249
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 3, 't': 17, 'action': None, 'reward': 2.1584854224881367, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.16)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'left')
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: -10.7836214298
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': -10.783621429753486, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.78)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: -10.8556021075
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'forward', 'reward': -10.855602107540623, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.86)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 296
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (7, 4), deadline = 20
0.324717404345
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3247; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: right, reward: 1.26572722905
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.265727229050771, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.27)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
1.66732572624
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 2.28679366979
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.2867936697938096, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.29)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'right', None)
2.15342076216
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 1.49955086578
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.4995508657772776, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.50)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', 'right')
2.38438136649
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 1.37849610188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.3784961018763886, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.38)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: forward, reward: -10.2747217329
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.274721732852367, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.27)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', 'forward')
1.21769524104
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: forward, reward: 1.70116831504
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.7011683150437544, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 1.70)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left')
2.00773298414
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 1.79318181175
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.793181811751095, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.79)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'right')
1.87942307782
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 1.44020980961
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.4402098096104903, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.44)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'right', 'right')
2.21990800289
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 1.25588552326
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.2558855232561263, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 1.26)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 2.57646899231
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.576468992305066, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.58)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
1.97705969802
Environment.act() [POST]: location: (1, 6), heading: (0, -1), action: None, reward: 0.948507516437
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 0.9485075164369301, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.95)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
0.98703441539
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 0.392204273074
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.3922042730738806, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.39)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'right')
1.61080573342
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: left, reward: 0.798980067178
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 0.7989800671777179, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 0.80)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
2.16120135401
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: 2.43148219933
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 2.4314821993254663, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.43)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'right', None)
1.38318409434
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 0.773686677036
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.773686677036322, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 0.77)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'forward', 'forward')
1.58044268768
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 0.930600135775
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 0.9306001357753841, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 0.93)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'forward')
2.00443974036
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 4), heading: (0, -1), action: right, reward: 1.01556445159
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.0155644515880724, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.02)
15% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 297
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (4, 2), deadline = 25
0.323485819702
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3235; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', None)
1.42290953041
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.41710123531
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.41710123530829, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.42)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.40224201007
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 1.62597912937
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.6259791293724117, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.63)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
2.13239665135
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 1.58713939127
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 1.5871393912721383, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.59)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: -10.7209606918
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -10.720960691778274, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.72)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.74223526191
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 1.33714868681
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.3371486868062414, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.34)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'forward', 'left')
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: 0.683165820777
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 0.6831658207774093, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded 0.68)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'left')
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: -9.07531488337
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -9.075314883365643, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.08)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
1.27502216711
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 1.58416139692
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.584161396920468, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 1.58)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'left')
1.66643597224
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: right, reward: 2.05727190566
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 2.057271905658883, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.06)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'right')
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: right, reward: 1.25536483164
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.2553648316422048, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.26)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'right', None)
1.79041523155
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: 2.11987203088
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.119872030883673, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.12)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.51411056972
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: forward, reward: 1.30697987559
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 1.3069798755910116, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.31)
52% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 298
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (3, 6), deadline = 20
0.322258906199
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3223; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: -4.16884055302
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': -4.168840553022191, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.17)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
1.42959178202
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: forward, reward: 0.979255026738
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 0.9792550267378309, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.98)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'right', 'forward')
2.13689865279
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 2.4865975483
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.4865975482968437, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 2.49)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
1.93214247071
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 2.5484263327
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.5484263327025833, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.55)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: right, reward: 1.45104749294
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.4510474929361363, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.45)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
1.69067471342
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 2.16112653332
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.1611265333164633, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.16)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: -9.85096390526
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': -9.850963905255075, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.85)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'forward')
1.51000209597
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 2.63581434439
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 2.635814344394352, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.64)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 1.30138251849
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.3013825184914436, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.30)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None)
1.92590062337
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: None, reward: 1.09513081479
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.0951308147930932, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.10)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
1.57417951493
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 1.31541831419
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.3154183141869902, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.32)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: -10.8492400153
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -10.849240015287785, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.85)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
2.21167967661
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 2.29741348971
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.297413489710178, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.30)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
1.41802513325
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: 0.901483222692
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 0.9014832226921075, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.90)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 2.18867588776
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.1886758877591355, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.19)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', 'right')
2.35356017822
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 2.4527328382
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 5, 't': 15, 'action': None, 'reward': 2.4527328381957103, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.45)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
1.53969197436
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: forward, reward: 1.52451149525
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 1.5245114952451941, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.52)
15% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 299
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (7, 2), deadline = 20
0.32103664612
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3210; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: 1.30919244892
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.3091924489165527, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.31)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: left, reward: -40.9246963755
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -40.92469637550314, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.92)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', 'forward')
1.67564020021
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.83131791701
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.831317917007443, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.83)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
1.42000538286
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 1.79905130579
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.7990513057864759, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.80)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.41054522266
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: 2.01365451143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.013654511434745, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.01)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
1.71209986705
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: forward, reward: 2.02723893359
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.02723893358593, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.03)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 300
\-------------------------
Environment.reset(): Trial set up with start = (1, 3), destination = (5, 5), deadline = 30
0.319819021816
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3198; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', None)
0.822256628016
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: forward, reward: 0.417881504421
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': 0.41788150442142014, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.42)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
2.25454658316
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.96632044388
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.9663204438799813, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.97)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
2.11043351352
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.99479036608
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.9947903660777655, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.99)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
2.0526119398
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 2.15252305134
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.1525230513425173, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.15)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
2.10256749557
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.08036836314
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.0803683631356826, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.08)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.59146792935
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.94728666165
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.9472866616480278, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.95)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.15975417797
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: left, reward: 1.6087301978
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 1.6087301978010142, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.61)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
2.23658706517
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.15018684234
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 23, 't': 7, 'action': None, 'reward': 2.1501868423434365, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.15)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
2.19338695376
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.172628207
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.1726282070042133, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.17)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', None)
2.18300758038
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.6979872303
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 21, 't': 9, 'action': None, 'reward': 1.6979872302955932, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.70)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: -19.6469295788
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 20, 't': 10, 'action': 'right', 'reward': -19.64692957879973, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.65)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
1.51423635866
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 1.158775188
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.1587751879972021, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.16)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: left, reward: 0.568521587173
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': 0.5685215871725907, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.57)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'left', None)
1.60952834432
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: right, reward: 2.33056643425
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 2.330566434253151, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.33)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: -5.15411891254
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': -5.154118912535523, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.15)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
1.86966940032
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: 2.40275289364
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 2.402752893643674, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.40)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, 'forward')
1.8285319143
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.3121167651
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.3121167651013113, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.31)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'right')
1.65981644372
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 2.327782799
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 13, 't': 17, 'action': None, 'reward': 2.3277827990025326, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.33)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'right', None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.22886411096
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 12, 't': 18, 'action': None, 'reward': 1.2288641109573462, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.23)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
1.7693772955
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.97952755756
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 19, 'action': None, 'reward': 1.9795275575640143, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.98)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
1.87445242653
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 2.4049862796
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 20, 'action': None, 'reward': 2.404986279599515, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.40)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'right', None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: -4.06903823484
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 9, 't': 21, 'action': None, 'reward': -4.0690382348419, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.07)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: -5.16712893112
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 8, 't': 22, 'action': None, 'reward': -5.167128931118473, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.17)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', 'right', None)
0.67737177461
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: -0.518434300666
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 7, 't': 23, 'action': 'right', 'reward': -0.5184343006658269, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded -0.52)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: 1.96358790591
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 6, 't': 24, 'action': 'forward', 'reward': 1.9635879059146333, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.96)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 0.610231167037
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 5, 't': 25, 'action': None, 'reward': 0.6102311670365972, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 0.61)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('forward', 'red', 'forward', None)
1.94049740534
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 1.49397282736
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 4, 't': 26, 'action': None, 'reward': 1.4939728273571087, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.49)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: left, reward: -19.5497524127
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 3, 't': 27, 'action': 'left', 'reward': -19.54975241265645, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.55)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('forward', 'green', None, 'forward')
1.86401306244
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: 0.635318734875
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 2, 't': 28, 'action': 'forward', 'reward': 0.635318734874821, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.64)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'red', None, 'forward')
2.30857054542
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 1.17085195727
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 1, 't': 29, 'action': None, 'reward': 1.1708519572719143, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.17)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 301
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (2, 5), deadline = 25
0.318606015705
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3186; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'right', 'forward')
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 2.12930664504
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.1293066450434006, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 2.13)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'right', None)
1.95514363122
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 2.64707456874
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.6470745687355186, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.65)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: -19.3466651879
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': -19.346665187938118, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.35)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: -39.0435996473
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': -39.0435996472632, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.04)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.5321017348
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: forward, reward: 1.69726636201
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.697266362009345, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.70)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (8, 4), heading: (0, 1), action: right, reward: 1.69326776395
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.6932677639514648, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent drove right instead of forward. (rewarded 1.69)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.38424218789
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: left, reward: 2.36726821139
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': 2.367268211389428, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.37)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', None)
1.33650577333
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 1.49745829528
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.4974582952806623, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.50)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
2.24028440171
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: 2.43137018795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 2.431370187951149, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.43)
64% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 302
\-------------------------
Environment.reset(): Trial set up with start = (1, 6), destination = (6, 3), deadline = 30
0.317397610269
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3174; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'left')
2.13840712779
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 2.5682954587
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 2.568295458704129, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.57)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'right', None)
1.62618247924
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 1.69083879164
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 1.6908387916370489, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.69)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 0.242548947217
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 0.24254894721661835, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.24)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
2.14250669327
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.2827933364
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.282793336404315, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.28)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
1.71265001484
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.70027932519
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.7002793251890678, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.70)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: forward, reward: -9.07444352593
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': -9.074443525931615, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent attempted driving forward through a red light. (rewarded -9.07)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
2.29634177667
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: left, reward: 2.74445304855
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 2.744453048548563, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.74)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
1.61468404841
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: 1.91013760726
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.9101376072554643, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.91)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
1.29706078486
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 2.49238950338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 22, 't': 8, 'action': 'left', 'reward': 2.492389503376012, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.49)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'left')
2.24182804994
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 2.20271170208
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.202711702084233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.20)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: -9.239120779
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': 'left', 'reward': -9.239120778998652, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.24)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 1.60016319466
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.60016319466419, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.60)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'left', 'forward')
1.4781292679
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: right, reward: 0.652468189729
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 18, 't': 12, 'action': 'right', 'reward': 0.6524681897287651, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent drove right instead of forward. (rewarded 0.65)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: -5.83556358199
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 17, 't': 13, 'action': None, 'reward': -5.835563581985503, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.84)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
1.87575519964
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: left, reward: 0.934402935202
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 14, 'action': 'left', 'reward': 0.9344029352023746, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.93)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: None, reward: 1.1056527445
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.1056527444988042, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.11)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
1.40507906742
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: left, reward: 2.70223811352
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 2.7022381135237556, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.70)
43% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 303
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (2, 6), deadline = 30
0.316193788061
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3162; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: left, reward: 1.75692999997
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 1.7569299999681087, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.76)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'left', None)
2.52039741261
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 1.80284006963
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 29, 't': 1, 'action': 'left', 'reward': 1.8028400696281368, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.80)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: -5.57278659645
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 28, 't': 2, 'action': None, 'reward': -5.5727865964453205, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.57)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
1.24966589866
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 1.43555327486
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 1.4355532748585198, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.44)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
1.36630755275
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.25255560309
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.252555603093932, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.25)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.76241082783
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 2.41017373342
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 2.4101737334207884, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.41)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'forward', None)
2.33582729483
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: 2.0566323304
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 2.056632330402776, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.06)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.82503136056
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 6), heading: (0, 1), action: forward, reward: 0.967133572397
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 0.9671335723970418, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.97)
73% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 304
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (8, 3), deadline = 30
0.314994531697
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3150; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'left')
2.23086435029
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 1.1771909013
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.1771909012976112, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.18)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'left')
1.70402762579
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 2.90318268806
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.903182688055985, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.90)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
2.20646467001
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 2.85953229761
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.859532297609329, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.86)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
2.16161874112
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: left, reward: 1.97519156121
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 27, 't': 3, 'action': 'left', 'reward': 1.9751915612081348, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.98)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
2.23836400321
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 1.13503260561
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.1350326056141207, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.14)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: right, reward: 0.917430780846
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 0.9174307808463983, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.92)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
2.13971935307
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 1.8437147127
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 6, 'action': None, 'reward': 1.843714712701079, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.84)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', 'right')
1.68242214069
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 1.56942507983
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.5694250798288605, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.57)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', 'forward')
1.89979017975
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 2.12377236168
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.1237723616782533, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.12)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
2.06840515116
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: left, reward: 2.44042761243
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 2.440427612432264, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.44)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
1.71723511635
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: 1.75741211238
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.7574121123812543, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.76)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
1.41698203431
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: forward, reward: 1.24946429226
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.2494642922574513, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.25)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: -4.50409236022
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': -4.504092360217384, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.50)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.39608246648
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 2.0908351833
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 2.090835183301029, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.09)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, 'left')
1.89472514412
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: left, reward: 1.08111902385
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 16, 't': 14, 'action': 'left', 'reward': 1.0811190238501813, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.08)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
1.74345882489
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: forward, reward: 2.25443148609
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 2.2544314860917307, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.25)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
1.99894515549
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: 1.76790311866
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 1.7679031186638485, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.77)
43% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 305
\-------------------------
Environment.reset(): Trial set up with start = (8, 3), destination = (4, 6), deadline = 35
0.313799823859
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3138; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
2.05365859047
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: left, reward: 1.44735775014
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 35, 't': 0, 'action': 'left', 'reward': 1.4473577501419097, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.45)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
1.34260958676
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 1.23349942936
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 34, 't': 1, 'action': 'forward', 'reward': 1.2334994293578898, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.23)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
1.85976802131
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 1.88248583165
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 33, 't': 2, 'action': 'forward', 'reward': 1.8824858316528488, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.88)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: 1.76914144761
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 32, 't': 3, 'action': 'left', 'reward': 1.7691414476135223, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent drove left instead of forward. (rewarded 1.77)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
1.51051571908
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 2.72222515441
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 31, 't': 4, 'action': None, 'reward': 2.7222251544119684, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.72)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: 1.76972799388
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 30, 't': 5, 'action': 'right', 'reward': 1.769727993876538, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.77)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', 'forward')
1.07614319919
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: left, reward: 1.49088015851
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 29, 't': 6, 'action': 'left', 'reward': 1.4908801585109017, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.49)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
2.08629228063
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: forward, reward: 2.77113366922
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 28, 't': 7, 'action': 'forward', 'reward': 2.771133669223804, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.77)
77% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 306
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (3, 4), deadline = 25
0.312609647296
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3126; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None)
1.80943157792
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.0187414923
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.0187414923013098, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.02)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
1.41408653511
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.77437119813
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.774371198129535, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.77)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.59422886662
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.49829514338
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.498295143382996, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.50)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.546262005
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 2.28538699687
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.285386996873908, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.29)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', 'forward')
2.14466080154
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.35064561549
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.350645615487406, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.35)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.51690845704
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.516908457040028, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.52)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.32437098561
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.3243709856110397, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.32)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
1.77332043081
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 2.73581445416
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.7358144541601535, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.74)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.68669830441
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 2.30377958378
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.303779583782977, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.30)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
1.33322316328
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 2.31850753566
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.318507535655665, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.32)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'right', 'forward')
2.18997903438
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: 1.93123703721
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.9312370372091525, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 1.93)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: None, reward: -4.67922541989
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': -4.679225419893784, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.68)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: forward, reward: 0.88487610013
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 0.8848761001301162, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.88)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, 'left')
1.86185393895
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: right, reward: 0.97710534933
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 0.9771053493297319, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 0.98)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'left', None)
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: right, reward: 1.68264596097
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.6826459609688151, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.68)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', 'forward')
1.28351167885
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: left, reward: 1.64015678173
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 1.6401567817301275, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.64)
36% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 307
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (7, 2), deadline = 20
0.311423984822
Simulating trial. . .
epsilon = 0.3114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3114; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
2.12633354403
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 1.05384435768
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.053844357679134, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.05)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
2.11637043675
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 2.91805349085
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.918053490851193, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.92)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
2.5172119638
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 1.54961026354
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.549610263539446, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.55)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
2.03341111367
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 1.47518605176
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.4751860517623971, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.48)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: right, reward: 1.68296376891
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.6829637689101649, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.68)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, 'left')
1.59008895085
Environment.act() [POST]: location: (2, 2), heading: (0, 1), action: None, reward: 1.74512492896
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.7451249289644166, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.75)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: right, reward: 2.45109589803
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 2.451095898025727, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.45)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: 2.60206206353
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.602062063527823, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 2.60)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'forward', None)
1.82586534947
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 2.78243838405
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 2.782438384053389, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.78)
55% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 308
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (5, 7), deadline = 25
0.310242819315
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3102; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 0.577940114164
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 0.5779401141643743, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.58)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'forward', 'left')
0.793400161829
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: forward, reward: 1.73990022915
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.7399002291525858, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded 1.74)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
1.2512752834
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: left, reward: 0.835458328825
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'left', 'reward': 0.8354583288250874, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.84)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
1.28805450806
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: 2.77786885053
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 2.77786885052719, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.78)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.88342413708
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 1.34924621207
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.3492462120737505, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.35)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'left')
1.87112692648
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 1.4598718791
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.4598718791021923, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.46)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
1.91582450094
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 1.39353304439
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.3935330443914262, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.39)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', 'right')
2.40314650821
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 2.38155461194
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.3815546119447415, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.38)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
1.65467877266
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 0.968550327466
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 0.96855032746636, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.97)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', 'right')
2.3804176663
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 2.27254756124
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.2725475612371326, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 2.27)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
2.00748861988
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 2.82434396557
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 2.8243439655673837, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.82)
56% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 309
\-------------------------
Environment.reset(): Trial set up with start = (5, 6), destination = (1, 2), deadline = 30
0.30906613372
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3091; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'forward')
1.68714536934
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: right, reward: 2.52167589933
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 2.5216758993300505, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.52)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 1.17699849622
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': 1.1769984962152447, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.18)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
1.66549940279
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: 1.56338412176
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': 1.56338412176128, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.56)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
2.03296167929
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: 2.54353596439
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 2.5435359643888784, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.54)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: right, reward: 1.01724655204
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 1.0172465520402048, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.02)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
1.9952389441
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 1.28914921301
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.2891492130090962, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.29)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
1.64219407855
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 1.35306764857
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 6, 'action': None, 'reward': 1.3530676485748876, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.35)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 0.929084803371
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 0.9290848033707969, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.93)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'forward')
2.50141164766
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: left, reward: 2.52791985165
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 22, 't': 8, 'action': 'left', 'reward': 2.527919851654873, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.53)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.75050817031
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: left, reward: 2.27082916021
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 2.2708291602101234, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.27)
67% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 310
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (5, 3), deadline = 20
0.307893911046
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3079; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'forward')
1.46183423029
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: left, reward: 2.97901417518
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 2.979014175179077, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.98)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
1.61444176228
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 2.29813946745
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 2.2981394674474984, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.30)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: 2.41270025119
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 2.412700251194682, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.41)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: None, reward: 2.52439565894
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.5243956589390604, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.52)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'left', None)
Environment.act() [POST]: location: (5, 2), heading: (1, 0), action: forward, reward: -10.399515405
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.399515405043038, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.40)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
1.67556380341
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: right, reward: 2.37972470596
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.3797247059591538, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.38)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 311
\-------------------------
Environment.reset(): Trial set up with start = (2, 4), destination = (4, 2), deadline = 20
0.306726134365
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3067; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: None, reward: 2.46699521866
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.466995218661059, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.47)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: right, reward: 1.35377738424
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.3537773842354377, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.35)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'right', 'left')
1.45237638024
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: forward, reward: 1.52082888288
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.5208288828848706, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 1.52)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
1.99171703288
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 1.47184169769
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.471841697686946, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.47)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: right, reward: 1.54438520769
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.5443852076894329, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.54)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', 'right')
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: right, reward: 1.34964655498
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.3496465549794427, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent drove right instead of forward. (rewarded 1.35)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: left, reward: -39.0859309535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -39.085930953495335, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.09)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.73177936529
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 0.912301281436
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 0.9123012814359945, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.91)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'right')
1.99379962136
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 2.64895374128
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.6489537412846866, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.65)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, 'right')
2.32137668132
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 2.60848209914
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.608482099143372, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.61)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, 'forward')
1.3379885421
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: None, reward: 1.06689770799
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.0668977079905562, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.07)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: -0.159932601687
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': -0.1599326016866316, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded -0.16)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, 'left')
1.66760693991
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 0.785641093634
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.785641093634136, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.79)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: forward, reward: 0.45336883608
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.45336883608012435, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.45)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
2.41591629272
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: right, reward: 2.231337192
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 2.2313371919953533, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.23)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', 'forward')
2.01178127071
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 0.571706773736
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.5717067737361883, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 0.57)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: left, reward: -9.48130214815
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': -9.481302148153738, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.48)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', 'forward')
2.22042420274
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: left, reward: 1.03326945208
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 1.033269452077847, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.03)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 312
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (2, 3), deadline = 20
0.305562786814
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3056; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', None)
2.35578338163
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 2.45168305353
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.4516830535268923, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.45)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'right', None)
2.30110909998
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.59868351002
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.598683510024689, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.60)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.49763086356
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.65380527616
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.6538052761612658, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.65)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 0.18486995812
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.18486995811971652, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.18)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
2.53299848381
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 1.7079728589
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.7079728589006562, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.71)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', 'left')
1.70607338641
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: left, reward: 2.13749843232
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 2.1374984323156223, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 2.14)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
2.01451771289
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 2.83695693062
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.836956930623887, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.84)
65% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 313
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (7, 6), deadline = 25
0.304403851596
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3044; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None)
1.31161455007
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.95627074934
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.956270749339474, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.96)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
2.1339426497
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.90515168998
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.9051516899805288, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.91)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
2.51954716984
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.90763295849
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.907632958486398, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.91)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.71359006416
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.29965250347
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.2996525034659039, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.30)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: -9.18861960506
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -9.188619605061838, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.19)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: 0.361506014572
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': 0.3615060145719923, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.36)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'forward')
1.43880379415
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: None, reward: 2.40598730983
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.4059873098319424, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.41)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, 'left')
1.38388600826
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 2.55579131487
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 2.5557913148652665, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.56)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', 'left')
1.34332435503
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 0.882819764558
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 17, 't': 8, 'action': None, 'reward': 0.8828197645576354, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent properly idled at a red light. (rewarded 0.88)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
2.00662128381
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.80110770534
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.801107705341729, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.80)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', 'forward')
1.18574034005
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 2.29279425948
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.292794259480995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.29)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -10.6353338136
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -10.63533381363564, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.64)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
1.73732361436
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 1.89691521891
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.896915218910359, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.90)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
2.30415186676
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: 1.95165127586
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.9516512758610822, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.95)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'forward')
2.10441063433
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 2.60003393258
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 2.6000339325841746, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.60)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, 'forward')
1.73971125135
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 0.731428292297
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 10, 't': 15, 'action': None, 'reward': 0.7314282922970421, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.73)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
2.42573732175
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: 2.33288372287
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 2.3328837228748123, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.33)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'forward')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: forward, reward: 0.884414576261
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 0.8844145762605724, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.88)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 314
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (4, 2), deadline = 20
0.303249311974
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3032; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 1.29368560635
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.2936856063510938, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.29)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.30786296486
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 2.37744746795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.3774474679545636, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.38)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'left')
1.90045739795
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 1.83399812829
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.8339981282895448, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.83)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'forward')
1.20244312505
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 2.24735723293
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.2473572329269795, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.25)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
1.84265521641
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 1.48572975104
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.4857297510412586, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.49)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
2.01066866526
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: left, reward: 1.23528457636
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.235284576362644, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.24)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', 'forward')
2.0606080358
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 1.33570708425
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.335707084253434, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 1.34)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.62297662081
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: left, reward: 1.12470178137
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.124701781366114, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.12)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'right', 'left')
1.48660263156
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: forward, reward: 2.56777603756
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 2.5677760375553973, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 2.57)
55% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 315
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (1, 7), deadline = 20
0.302099151278
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3021; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.66419248372
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: None, reward: 2.63728484264
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.6372848426429676, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.64)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
2.15073866318
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: None, reward: 2.15653362417
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.1565336241664346, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.16)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: forward, reward: -10.2349029878
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.234902987836852, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.23)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 1.48536738812
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.4853673881221372, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.49)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'right', None)
1.52767496246
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 2.42213344169
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.422133441692882, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.42)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
0.689619344232
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 0.399745845549
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.3997458455494063, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.40)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
1.13081482518
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 2.81066565186
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 2.8106656518566484, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.81)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
2.37931052231
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: 2.37350561852
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.3735056185201744, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.37)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', None)
2.449896305
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 1.90303810816
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.903038108158232, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.90)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
2.37640807042
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 2.04672572658
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 2.046725726575067, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.05)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'right')
1.47296320071
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: 1.82731682252
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.8273168225190106, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 1.83)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'forward', None)
2.40373321758
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: right, reward: 1.64052854909
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.6405285490895734, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.64)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, 'right')
1.15072496013
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 1.51260490904
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.5126049090386837, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.51)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
1.57571806986
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 0.641109899562
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.6411098995624476, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.64)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'left')
2.25456744249
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: None, reward: 1.60944078166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.6094407816613248, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.61)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: forward, reward: -9.66103958299
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -9.661039582987124, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.66)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
2.2115668985
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: forward, reward: 0.532302044974
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.5323020449739568, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.53)
15% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 316
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (4, 7), deadline = 20
0.300953352899
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
Simulating trial. . .
epsilon = 0.3010; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'left')
0.533508563765
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: forward, reward: 1.08413795735
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.0841379573467103, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 1.08)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', 'right', 'left')
0.672678213814
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: forward, reward: 1.21559615495
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 1.215596154951708, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove forward instead of left. (rewarded 1.22)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'forward')
1.72490017899
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: None, reward: 2.34648415568
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.346484155677695, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.35)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: -9.26385212952
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': -9.263852129519313, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.26)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 0.652120826448
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.6521208264484634, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.65)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
2.15363614367
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 1.65373686255
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.6537368625469586, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.65)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.90368650311
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 2.35072330057
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.350723300568216, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.35)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 1.44001117375
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.4400111737532855, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.44)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: -4.75100153104
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 12, 't': 8, 'action': None, 'reward': -4.751001531039421, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.75)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, 'forward')
1.92239555199
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: 0.885519964513
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 11, 't': 9, 'action': None, 'reward': 0.8855199645133336, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.89)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None)
1.97004738929
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: 1.16956149805
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.1695614980508726, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.17)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: forward, reward: 1.78719601125
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.78719601124694, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.79)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'right', 'left')
1.11307205979
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 1.11470284487
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.1147028448655079, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent properly idled at a red light. (rewarded 1.11)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
2.12790157131
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 1.77643808582
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.7764380858153055, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.78)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, 'left')
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: -40.3648967944
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -40.36489679444959, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.36)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'right', None)
1.65851063544
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 0.799078262616
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.7990782626161634, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 0.80)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: None, reward: 1.02813251195
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.0281325119544609, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.03)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', 'right')
1.2182937464
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: right, reward: 0.170198020058
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.1701980200578811, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent drove right instead of forward. (rewarded 0.17)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, 'forward')
2.51466574966
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: left, reward: 1.51623838286
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 2, 't': 18, 'action': 'left', 'reward': 1.5162383828554455, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.52)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, 'right')
2.46492939023
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: 1.73414050309
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.73414050309142, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.73)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 317
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (8, 5), deadline = 25
0.299811900292
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2998; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'right', 'forward')
1.61764491905
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 2.63810732526
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.63810732525604, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 2.64)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
1.10841398471
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 1.2131092639
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.213109263895717, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.21)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 1.12219429512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.1221942951242792, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.12)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
2.12720490184
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: 1.67815539566
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.6781553956635886, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.68)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
1.37383920109
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: 2.73309296603
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 2.733092966026999, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.73)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
1.95216982856
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: 2.631058676
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.63105867600106, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.63)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'forward')
1.23556977182
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.95113707344
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.951137073441587, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.95)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', 'left')
2.42285664399
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.14373555785
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.1437355578487993, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.14)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: left, reward: -40.2499747243
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -40.249974724349016, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.25)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.90386449458
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 2.13860098901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.1386009890138644, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.14)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: -9.75723752331
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': -9.757237523312718, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent attempted driving forward through a red light. (rewarded -9.76)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', 'right')
2.32648261377
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: 1.60301877309
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 1.6030187730946521, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 1.60)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
1.97074023852
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 2.21160336991
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 2.2116033699103204, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.21)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.57956524149
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: forward, reward: 1.4850783597
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.4850783596970816, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.49)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: left, reward: -9.75828068711
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -9.758280687106874, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.76)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
1.1607616243
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 0.932505595376
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 0.9325055953763617, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.93)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'forward', 'forward')
1.25552141173
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: forward, reward: 1.46518938479
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 1.4651893847881532, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 1.47)
32% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 318
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (7, 6), deadline = 35
0.298674776973
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2987; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, None)
1.53232180059
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: 1.24863419154
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 35, 't': 0, 'action': 'forward', 'reward': 1.2486341915384498, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.25)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'right', 'left')
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: forward, reward: 2.7194138695
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 34, 't': 1, 'action': 'forward', 'reward': 2.7194138695027874, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 2.72)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
1.39047799607
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 1.89347359364
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 33, 't': 2, 'action': 'forward', 'reward': 1.893473593641005, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.89)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'right', None)
2.17646720658
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 2.43658204412
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 32, 't': 3, 'action': None, 'reward': 2.4365820441175963, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.44)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: -9.64796332116
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 31, 't': 4, 'action': 'left', 'reward': -9.647963321158752, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.65)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
1.64197579485
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: 2.31695861189
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 30, 't': 5, 'action': 'forward', 'reward': 2.3169586118909455, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.32)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None)
2.02764425469
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 1.98929856993
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 29, 't': 6, 'action': 'right', 'reward': 1.989298569927351, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.99)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', 'forward')
1.74765320851
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 1.76031177584
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 28, 't': 7, 'action': None, 'reward': 1.7603117758400366, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.76)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
2.0212327418
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 2.72872174662
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 27, 't': 8, 'action': None, 'reward': 2.7287217466226883, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.73)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
2.37497724421
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 1.71569972217
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 26, 't': 9, 'action': None, 'reward': 1.7156997221747001, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.72)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
2.04533848319
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 2.02844116565
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 10, 'action': None, 'reward': 2.02844116564913, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.03)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
1.97281071598
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: forward, reward: 1.81895078914
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 24, 't': 11, 'action': 'forward', 'reward': 1.8189507891408936, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.82)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'forward')
1.58633169905
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: forward, reward: 2.79313228271
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 23, 't': 12, 'action': 'forward', 'reward': 2.7931322827123717, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.79)
63% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 319
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (2, 4), deadline = 20
0.297541966524
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2975; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
2.05346608356
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: left, reward: 1.76186791148
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.7618679114846936, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.76)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
1.04663360984
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.12959664382
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.1295966438155969, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.13)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.08811512683
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.64119089154
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.641190891543978, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.64)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.36465300919
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.6337012356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.6337012356010285, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.63)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.97946720337
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 1.7492479918
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.7492479918000159, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.75)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward')
2.03569216733
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.26084357961
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.26084357960569, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.26)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: -20.9934875373
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': -20.993487537333824, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.99)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', 'left')
2.30360515692
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.30143569198
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.3014356919760695, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.30)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: left, reward: -10.6046561518
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -10.604656151796151, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.60)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
2.2544163818
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: left, reward: 1.40140120029
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.4014012002891647, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.40)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: left, reward: -0.0323577916983
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -0.03235779169828901, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove left instead of forward. (rewarded -0.03)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: left, reward: -39.2901885949
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': -39.2901885948838, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.29)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
2.32362674236
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: 0.725146612433
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 0.725146612432567, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.73)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: forward, reward: 0.0349416205245
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.034941620524517325, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent drove forward instead of right. (rewarded 0.03)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
2.09117180422
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: right, reward: 1.26613305195
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.2661330519472362, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.27)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
2.30317116974
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.37129683124
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.3712968312404472, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.37)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, 'left')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: -9.43411539963
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': -9.43411539963036, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -9.43)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
1.67865242808
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 0.448706965808
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.4487069658080618, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.45)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 320
\-------------------------
Environment.reset(): Trial set up with start = (3, 6), destination = (7, 5), deadline = 25
0.296413452585
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2964; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'left')
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: left, reward: -9.68200714206
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 25, 't': 0, 'action': 'left', 'reward': -9.68200714206403, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -9.68)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
1.98297890325
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: right, reward: 1.34908006487
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.3490800648674475, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.35)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
1.95629061486
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: forward, reward: 1.58402149006
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 1.5840214900591478, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.58)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: 0.35622579958
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 22, 't': 3, 'action': 'left', 'reward': 0.3562257995800242, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.36)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 1.99722595743
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.9972259574267845, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.00)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, 'left')
1.41947964414
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 2.58866675896
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 2.5886667589614385, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.59)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', 'left')
1.31771271815
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 1.43591591188
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.435915911879776, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.44)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'right', None)
1.22879444903
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: right, reward: 1.65339458112
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.6533945811170205, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.65)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
1.86435759759
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: forward, reward: 0.949051050199
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 0.9490510501993124, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.95)
64% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 321
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (1, 5), deadline = 20
0.295289218862
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2953; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.33001491361
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.3300149136099375, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.33)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: -10.1158417482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.115841748188394, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.12)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
2.12048567136
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.8879548303
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.8879548303010179, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.89)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
2.00422025083
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.5817832447
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.581783244703465, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.58)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: forward, reward: -10.7043435846
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.704343584635728, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.70)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
1.82790879104
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: 2.72196992777
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 2.721969927768992, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.72)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'forward')
2.18973199088
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 1.79951461794
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.7995146179383743, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.80)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: left, reward: -9.64535461777
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -9.645354617767115, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.65)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.49917712239
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 1.10363508985
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.1036350898476892, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.10)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.30140610612
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 1.22948144169
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.2294814416885116, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.23)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.40670432389
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: 1.85364795212
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.8536479521190528, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.85)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
1.2654437739
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.30400394366
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.3040039436560513, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.30)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
2.03688982442
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.15691672698
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.1569167269846794, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.16)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'left', None)
1.5969032757
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.88049955368
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.880499553678854, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.88)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: forward, reward: -10.0084768847
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': -10.008476884704132, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.01)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (8, 6), heading: (1, 0), action: None, reward: 1.57270825578
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.5727082557840073, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.57)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
1.89588075256
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: 0.436545140548
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'forward', 'reward': 0.4365451405479104, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.44)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
2.27493935941
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: left, reward: 1.47817887351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 1.4781788735076444, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.48)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 322
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (4, 2), deadline = 25
0.294169249121
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2942; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.90268014875
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 2.8953220812
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.895322081201222, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.90)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 1.79628662825
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.796286628250854, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.80)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 1.77204290232
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.7720429023213518, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.77)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 0.0182704078508
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 0.018270407850783155, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.02)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: 1.66300976373
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.663009763729356, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.66)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'left', 'forward')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: -19.940661696
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': -19.940661696013912, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.94)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None)
2.00847141231
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: 1.62749531603
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.6274953160340264, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.63)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'left', None)
1.81798336417
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 1.19987586129
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.1998758612949771, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.20)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'left', 'right')
1.96475069343
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 1.06576192301
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.0657619230074145, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 1.07)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'forward')
1.99462330441
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 2.00858470539
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.00858470538572, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.01)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
1.28472385878
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: None, reward: 1.17200628898
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.172006288982042, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.17)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
1.22836507388
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: None, reward: 2.25595162888
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.2559516288837425, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.26)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: right, reward: -0.150845015452
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': -0.15084501545152462, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded -0.15)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: left, reward: 1.43502359956
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 1.4350235995627365, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.44)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: left, reward: -19.6877132978
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -19.68771329781269, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.69)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
1.90766699752
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: left, reward: 1.21909673255
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 1.21909673254582, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.22)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'forward')
2.0016040049
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: forward, reward: 1.20074680933
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 1.2007468093313893, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.20)
32% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 323
\-------------------------
Environment.reset(): Trial set up with start = (8, 3), destination = (5, 6), deadline = 30
0.293053527189
Simulating trial. . .
epsilon = 0.2931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2931; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2931; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'left')
1.45311214606
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 1.46123071082
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.4612307108159734, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.46)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
1.66602948406
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 2.48373201089
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 2.483732010889999, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.48)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: -4.47436362212
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 28, 't': 2, 'action': None, 'reward': -4.474363622117351, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.47)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'right', 'left')
2.37330160203
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 2.00441016172
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 2.0044101617180408, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 2.00)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: -10.5074864148
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': -10.507486414806369, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.51)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 2.13589670373
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 2.1358967037252494, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.14)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', 'left')
1.78329610092
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 1.13776841723
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 24, 't': 6, 'action': None, 'reward': 1.137768417226347, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.14)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
1.31492258467
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: None, reward: 1.44982798571
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.449827985714819, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.45)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', 'forward', None)
2.29161425228
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: forward, reward: 2.55582275588
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': 2.555822755884473, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.56)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'forward')
2.35222228346
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: right, reward: 1.10815722072
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 1.1081572207242054, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.11)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
1.74215835138
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: 1.63277308017
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.6327730801711648, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.63)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'right')
1.65014001161
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: forward, reward: 2.60650118231
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 2.606501182311048, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 2.61)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'left')
1.77015605246
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: 2.80217313148
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 2.8021731314760787, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.80)
57% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 324
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (7, 2), deadline = 20
0.291942036954
Simulating trial. . .
epsilon = 0.2919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2919; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2919; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'forward')
1.40395775825
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 2.30343664706
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.303436647064284, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.30)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 2.53476662663
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.5347666266337296, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.53)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
1.56980444367
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 2.54408502599
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.544085025988954, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.54)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.93729905921
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.11295999482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.112959994818309, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.11)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', 'forward')
1.66334537398
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.6393721263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.6393721262992809, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.64)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.38237528519
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 0.162753511886
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 0.16275351188591447, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.16)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
1.16621294655
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: 2.80039212598
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.8003921259763027, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.80)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: right, reward: 1.66814496657
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.668144966570139, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent drove right instead of forward. (rewarded 1.67)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
2.09764387161
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 2.56593766749
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.565937667490493, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.57)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
1.65579135801
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: 2.37632033433
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 2.3763203343341353, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.38)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.63017613801
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 1.1286226605
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.1286226604972105, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.13)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
2.33179076955
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.40821109233
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.408211092325816, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.41)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
2.37000093094
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.49425696992
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.494256969916953, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.49)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
2.01605584617
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: left, reward: 1.0895346387
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.089534638700289, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.09)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 325
\-------------------------
Environment.reset(): Trial set up with start = (8, 5), destination = (5, 7), deadline = 25
0.290834762368
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2908; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'right', None)
1.44109451507
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 1.79170496523
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.7917049652263886, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.79)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', 'left')
1.46053225907
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.6767485087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.67674850869531, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.68)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.6309547719
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.6309547719044593, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.63)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.82804214946
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.38638911228
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.386389112276059, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.39)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
1.60721563087
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.8311507185
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.8311507184983025, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.83)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', 'forward')
1.73926729976
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 1.42736287329
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.427362873288457, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 1.43)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
1.37939939925
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 1.02598436985
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.0259843698453648, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.03)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: -40.8207231713
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': -40.820723171342166, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.82)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
2.43212895043
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: 1.43023966348
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.4302396634752248, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.43)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'left')
1.48792208399
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: 1.40883595756
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 1.408835957561999, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.41)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
1.68746571578
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.9707854125
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.9707854125016506, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.97)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
1.82912556414
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.43955269747
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.4395526974663793, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.44)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: -40.4507041918
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -40.45070419179485, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.45)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.20269188455
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: forward, reward: 1.69511519634
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.6951151963358195, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.70)
44% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 326
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (5, 3), deadline = 20
0.289731687441
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2897; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
1.55279524244
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: left, reward: 1.41621964047
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.416219640471556, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.42)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
1.60117540711
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 2.90568482113
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 2.90568482112721, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.91)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', 'forward', 'right')
0.913463795158
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 0.544387761692
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 0.5443877616919167, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove forward instead of left. (rewarded 0.54)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', 'right')
0.68497931247
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: forward, reward: 1.74517430607
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.7451743060663887, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 1.75)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, 'right')
2.09953494666
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 2.73607274016
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.7360727401611067, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.74)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.93118430695
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 1.51216473304
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.5121647330362418, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.51)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.72167451999
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: None, reward: 2.06448390836
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.0644839083643673, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.06)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'right', None)
0.0794687369721
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: right, reward: 1.84000223054
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.8400022305353974, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.84)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
2.07488074747
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: right, reward: 1.25620997
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.2562099700026996, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.26)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
1.44890354044
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: forward, reward: 2.44216102885
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 2.442161028847751, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.44)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', 'forward')
2.25347905861
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 2.53161885018
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.5316188501811756, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.53)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: right, reward: 2.53577817404
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 2.535778174042921, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.54)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'right')
2.12832059696
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: forward, reward: 0.9111761706
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.9111761705999777, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 0.91)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
1.6343391308
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 0.85148432807
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.8514843280697462, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.85)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
1.24291172944
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 0.592332666626
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.5923326666262814, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.59)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'right', None)
1.84024872461
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: forward, reward: 2.37260016158
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 2.372600161583415, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 2.37)
20% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 327
\-------------------------
Environment.reset(): Trial set up with start = (4, 3), destination = (8, 6), deadline = 35
0.288632796244
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2886; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: None, reward: -4.53258719213
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 35, 't': 0, 'action': None, 'reward': -4.532587192129435, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.53)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: forward, reward: 2.34003485443
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 34, 't': 1, 'action': 'forward', 'reward': 2.3400348544314467, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.34)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: left, reward: 1.20459471426
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 33, 't': 2, 'action': 'left', 'reward': 1.2045947142647127, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.20)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
2.29636145444
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: right, reward: 1.58890465676
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 32, 't': 3, 'action': 'right', 'reward': 1.588904656761869, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.59)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
2.14278356954
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 1.7733021793
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 31, 't': 4, 'action': 'forward', 'reward': 1.773302179296338, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.77)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.98330253627
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 1.12763396208
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 30, 't': 5, 'action': 'forward', 'reward': 1.1276339620808977, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.13)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, 'forward')
2.01545206626
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: 1.30930378957
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 29, 't': 6, 'action': 'left', 'reward': 1.3093037895701871, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.31)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
1.55546824917
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: forward, reward: 1.26106147108
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 28, 't': 7, 'action': 'forward', 'reward': 1.2610614710843604, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.26)
77% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 328
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (1, 5), deadline = 20
0.28753807291
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2875; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.91722997896
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: None, reward: 2.28778130065
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.2877813006501078, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.29)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
1.66554535874
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 1.97448748698
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.9744874869812155, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.97)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
2.25343011412
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: forward, reward: 1.80334047955
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.8033404795545211, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.80)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'forward')
1.66237792791
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: 0.982970537207
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 0.9829705372070487, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 0.98)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 1.70900249077
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.7090024907727601, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.71)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
1.95804287442
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: forward, reward: 2.56670556593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.5667055659290687, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.57)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 329
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (7, 5), deadline = 25
0.286447501631
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2864; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', None)
1.40826486013
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: forward, reward: 2.93522947015
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 2.9352294701527826, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.94)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
2.26237422017
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 2.4578782142
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 2.4578782141995417, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.46)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', 'right', None)
2.57342062219
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 1.99726137915
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.9972613791507108, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 2.00)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.71918317468
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 2.27955881748
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.2795588174772776, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.28)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: None, reward: 1.91806041024
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.9180604102388978, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.92)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', 'forward')
1.58331508653
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: forward, reward: 2.32282208998
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.3228220899774112, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.32)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: left, reward: -39.8245715239
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -39.82457152391441, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.82)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', 'forward')
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 5), heading: (0, -1), action: forward, reward: 2.40799930587
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 2.407999305866294, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 2.41)
68% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 330
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (7, 6), deadline = 20
0.285361066658
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2854; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'right')
1.21507680927
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: forward, reward: 0.926000272701
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 0.9260002727008609, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 0.93)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
1.56338186503
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: 1.21909470302
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 1.2190947030238468, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.22)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: 1.71596216496
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.7159621649555024, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 1.72)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'forward')
1.32267423256
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: left, reward: 1.84380556967
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.8438055696732403, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.84)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
1.95871570316
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: None, reward: 2.07085148063
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.070851480627846, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.07)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
2.17174716514
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: forward, reward: 2.54859498791
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.5485949879093073, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.55)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'left', None)
1.9426330556
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 1.60594294117
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.6059429411653754, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.61)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: left, reward: -9.74149733349
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -9.741497333494035, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.74)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
2.01478359189
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 1.75092703914
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.7509270391437002, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.75)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.88285531552
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 2.72828268839
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.728282688386039, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.73)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', 'right')
2.39235056008
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 1.70784568503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.707845685034444, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.71)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 1.21647286436
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.2164728643646259, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.22)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
1.89307921418
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.00574540732
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.0057454073207452, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.01)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
1.5353970111
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: -0.187409777004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': -0.18740977700362527, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded -0.19)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
2.1025056398
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 0.780476295364
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.7804762953642304, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.78)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.5243866774
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 0.999745282982
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.9997452829818998, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.00)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'right', 'forward')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.98365039764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.9836503976395439, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.98)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', None)
1.77428799838
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 1.43674008885
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 1.4367400888456028, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.44)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: left, reward: -9.27745013935
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': 'left', 'reward': -9.277450139351245, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.28)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', None)
2.30556900195
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 0.740429910996
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.7404299109955572, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.74)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 331
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (3, 6), deadline = 30
0.284278752304
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2843; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'left')
1.86722776312
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 1.39846967087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.3984696708718973, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.40)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, 'left')
1.63284871699
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: None, reward: 1.3399312875
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.339931287503808, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.34)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 1.27180738961
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 1.2718073896110398, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent drove right instead of left. (rewarded 1.27)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
2.36012621719
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 1.79057773565
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 1.7905777356504853, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.79)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 0.881615680066
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 0.881615680065918, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.88)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'left')
1.48639000225
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 2.03591842375
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 25, 't': 5, 'action': None, 'reward': 2.0359184237492753, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.04)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'forward')
2.14826787347
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 1.18132704404
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 24, 't': 6, 'action': None, 'reward': 1.181327044039153, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.18)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'right')
1.2048929003
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: left, reward: 2.05726025248
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 2.057260252476043, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.06)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: 0.200732480396
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 22, 't': 8, 'action': 'right', 'reward': 0.20073248039582092, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded 0.20)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
1.44941231075
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 0.923326244809
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': 0.9233262448091861, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.92)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', 'left')
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: -40.8524058548
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 20, 't': 10, 'action': 'left', 'reward': -40.85240585477781, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.85)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
0.620069066219
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: 0.299392983682
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 0.2993929836820727, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.30)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
1.39123828403
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: left, reward: 1.6445433103
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': 1.644543310295124, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.64)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: left, reward: 1.48868930601
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'left', 'reward': 1.4886893060098156, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.49)
53% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 332
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (6, 2), deadline = 20
0.283200542941
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2832; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.44149096758
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.02003951026
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.020039510259549, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.02)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
1.73076523892
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.83786720937
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.8378672093685045, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.84)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'right', None)
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: left, reward: -40.3789764122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -40.37897641224905, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.38)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, 'left')
1.96983866156
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 2.32911910034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.3291191003369844, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.33)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, 'left')
2.14947888095
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 2.58612975396
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 2.586129753957663, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.59)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
1.48450744145
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 2.70404636466
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 2.704046364657497, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.70)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
1.3133123444
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: None, reward: 1.66474921712
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.6647492171204012, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.66)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
2.07535197642
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 1.15417773697
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.1541777369687891, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.15)
60% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 333
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (1, 5), deadline = 30
0.282126422998
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2821; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', None)
2.42371850408
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: 2.72338922852
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': 2.7233892285217856, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.72)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 0.963625762248
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 0.9636257622481265, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.96)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: -9.65150398004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 28, 't': 2, 'action': 'left', 'reward': -9.651503980040378, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.65)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
1.18636927778
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.70859678654
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.708596786538484, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.71)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
1.50329005159
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: 1.71320192926
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': 1.7132019292640939, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.71)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 1.96220577343
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.9622057734255192, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.96)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', None)
2.1064244431
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 1.43487307207
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 1.4348730720662768, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.43)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, None)
1.35318828492
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: left, reward: 1.01065295181
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 1.010652951808603, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 1.01)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
2.28431622415
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 1.47270642894
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.4727064289437055, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.47)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, 'forward')
1.85369720266
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 2.8383308322
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.8383308321968164, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.84)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: left, reward: -9.34236338444
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 10, 'action': 'left', 'reward': -9.342363384442224, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.34)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
1.18192061836
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: -0.0931467726476
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': -0.09314677264764593, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded -0.09)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: left, reward: -9.66785821654
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': -9.66785821654498, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.67)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
0.544682594891
Environment.act() [POST]: location: (2, 3), heading: (0, 1), action: right, reward: 1.30741042158
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 1.3074104215849045, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.31)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'right', None)
1.61639974015
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 0.899943437089
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 16, 't': 14, 'action': 'right', 'reward': 0.8999434370885984, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 0.90)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
1.44748303216
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.23664488801
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.2366448880057994, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.24)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: 2.0583371873
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'left', 'reward': 2.0583371872971723, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.06)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', 'left', 'forward')
1.65135875014
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 1.2761227454
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 13, 't': 17, 'action': None, 'reward': 1.2761227454011295, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.28)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', 'left')
2.06864038388
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 1.10032101217
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 12, 't': 18, 'action': None, 'reward': 1.100321012168047, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.10)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', None)
1.52299945647
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 2.22754900022
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.2275490002158627, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.23)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', None)
1.87527422834
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: None, reward: 2.34460882886
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 20, 'action': None, 'reward': 2.3446088288582976, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.34)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
2.36017107653
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: forward, reward: 1.48110487867
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 1.4811048786720673, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.48)
27% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 334
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (3, 4), deadline = 20
0.281056376966
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2811; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'right', None)
2.28534100067
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: 2.69816174839
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.6981617483855986, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 2.70)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: right, reward: 2.8226096245
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.822609624499629, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.82)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
2.28616459197
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 1.52001797456
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.5200179745625306, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.52)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
1.61476485669
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 1.22110508708
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.2211050870845368, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.22)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: left, reward: 0.787372209762
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 0.78737220976205, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.79)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: forward, reward: 0.816134005299
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 0.8161340052990325, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.82)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'left')
2.36780431745
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 1.62835144886
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.6283514488552457, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.63)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, 'right')
1.63218765645
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 2.15770371241
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 2.157703712413586, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent followed the waypoint right. (rewarded 2.16)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: forward, reward: 1.32573896387
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.3257389638657118, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.33)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: -40.4396805069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': -40.4396805068933, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.44)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
1.9206379776
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 4), heading: (0, 1), action: forward, reward: 2.72882008332
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 2.7288200833219998, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.73)
45% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 335
\-------------------------
Environment.reset(): Trial set up with start = (4, 5), destination = (8, 6), deadline = 25
0.279990389393
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2800; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (4, 4), heading: (0, -1), action: right, reward: 1.43654850857
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.436548508570971, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent drove right instead of forward. (rewarded 1.44)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
1.83329158886
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: 2.32452161442
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 2.3245216144225234, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.32)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: 1.16933592518
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 1.1693359251754472, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.17)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
1.27058644653
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 2.21162692023
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 2.211626920229859, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.21)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
2.1099415286
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.69023926628
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.690239266276618, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.69)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
2.32472903046
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 2.46151617255
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.461516172549661, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.46)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left')
1.761154213
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.12865793139
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.128657931393272, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.13)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'left')
1.4449060722
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.32340269399
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.3234026939883559, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.32)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.34206396008
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.824165032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.8241650320000717, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.82)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
2.08311449604
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 0.962356241535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 0.9623562415348743, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.96)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'right', None)
1.97490420208
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.72537628823
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.725376288233656, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.73)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
2.07890660164
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: 0.88493154441
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 0.8849315444104187, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.88)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.74110668338
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: forward, reward: 0.883218991327
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 0.8832189913274555, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.88)
48% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 336
\-------------------------
Environment.reset(): Trial set up with start = (5, 6), destination = (1, 3), deadline = 35
0.278928444885
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2789; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: 1.79983822114
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 35, 't': 0, 'action': 'left', 'reward': 1.7998382211370179, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.80)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.31216283735
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: forward, reward: 2.02590889044
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 34, 't': 1, 'action': 'forward', 'reward': 2.0259088904367526, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.03)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.90009039744
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 2.72691770309
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 33, 't': 2, 'action': None, 'reward': 2.726917703090606, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.73)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.31350405026
Environment.act() [POST]: location: (7, 6), heading: (1, 0), action: None, reward: 2.14831656102
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 32, 't': 3, 'action': None, 'reward': 2.1483165610179604, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.15)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: right, reward: 0.356368017128
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 31, 't': 4, 'action': 'right', 'reward': 0.35636801712803146, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.36)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.52273536879
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: None, reward: 2.06645877503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 30, 't': 5, 'action': None, 'reward': 2.066458775028872, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: -9.93098589382
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 29, 't': 6, 'action': 'forward', 'reward': -9.930985893824616, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.93)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.79459707191
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: None, reward: 1.18807529161
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 28, 't': 7, 'action': None, 'reward': 1.1880752916091866, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.19)
77% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.49133618176
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: None, reward: 2.50057222362
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 27, 't': 8, 'action': None, 'reward': 2.5005722236173007, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.50)
74% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.64087864708
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: left, reward: 2.5848369982
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 26, 't': 9, 'action': 'left', 'reward': 2.5848369981970505, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.58)
71% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 2), heading: (0, 1), action: right, reward: 0.302997351297
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 25, 't': 10, 'action': 'right', 'reward': 0.3029973512970091, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove right instead of forward. (rewarded 0.30)
69% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', 'left')
0.808823260556
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: forward, reward: 1.07143222539
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 24, 't': 11, 'action': 'forward', 'reward': 1.0714322253938429, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 1.07)
66% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 1.45432413883
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 23, 't': 12, 'action': 'right', 'reward': 1.4543241388338868, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove right instead of left. (rewarded 1.45)
63% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: left, reward: 0.06447140103
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 22, 't': 13, 'action': 'left', 'reward': 0.06447140103001081, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove left instead of right. (rewarded 0.06)
60% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
2.11285782264
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 1.25868952842
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 14, 'action': 'left', 'reward': 1.2586895284205002, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.26)
57% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'left', 'left')
1.33124789218
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 2.19547996917
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 20, 't': 15, 'action': 'forward', 'reward': 2.1954799691709797, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.20)
54% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
1.79300174777
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 1.24080014048
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 16, 'action': None, 'reward': 1.2408001404789946, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.24)
51% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'left', None)
2.09427690306
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: left, reward: 1.50816858984
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 18, 't': 17, 'action': 'left', 'reward': 1.508168589841548, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.51)
49% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 337
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (7, 7), deadline = 20
0.27787052811
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2779; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'left')
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: right, reward: 2.3585618541
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.358561854097058, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.36)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
1.60551404361
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: 2.60592741768
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.605927417676221, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.61)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
1.66903586389
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: 1.89516107544
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.895161075441845, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.90)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.48903078076
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 2.32350714728
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.3235071472836717, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.32)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.90626896402
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 2.57639449553
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.576394495528261, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.58)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 0.200562318359
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 0.20056231835892868, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.20)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: left, reward: -39.581666739
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -39.58166673899598, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.58)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', None)
1.51690094412
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.19140128764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.1914012876408304, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.19)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
1.35415111588
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.87777485373
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.8777748537308268, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.88)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None)
1.61596298481
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.94585209542
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.9458520954184022, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.95)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.75307728634
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.7530772863390423, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.75)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', 'left')
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 0.658505741569
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.6585057415693951, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 0.66)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: 1.45356034254
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 1.4535603425446808, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.45)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', 'left', 'left')
1.80252042445
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.12841231982
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.1284123198171656, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.13)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.3600784282
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.360078428203105, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.36)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: -9.08837792133
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': -9.088377921334384, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.09)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
1.80122274645
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: 1.28005029121
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 1.2800502912083638, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.28)
15% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 338
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (5, 4), deadline = 20
0.276816623789
Simulating trial. . .
epsilon = 0.2768; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2768; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2768; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2768; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2768; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2768; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'right')
1.88143873418
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 1.86830488379
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.868304883791855, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.87)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 1.79781325593
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.7978132559262832, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.80)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 1.66735378763
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.6673537876295037, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.67)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'forward', None)
0.926046508238
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: 0.378766405157
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.3787664051571239, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.38)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, None)
1.06367969694
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 1.43320448621
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.4332044862056772, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.43)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
1.68577367553
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: 2.29097585953
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 2.2909758595303518, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.29)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 1.83268919152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.8326891915241104, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.83)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.99595420269
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 2.25702495174
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.257024951738151, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.26)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
2.12648957721
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 2.78494162111
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.7849416211108906, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.78)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
2.45571559916
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 1.09484764806
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.0948476480623968, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.09)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: -5.47294834341
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': -5.472948343405335, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.47)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
1.98837476753
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: left, reward: 2.24552566109
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 2.2455256610894696, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.25)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'left', 'right')
1.07053854098
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: forward, reward: -0.0279439283362
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': -0.0279439283361953, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded -0.03)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'forward')
1.66479745875
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: None, reward: 2.61442155263
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 7, 't': 13, 'action': None, 'reward': 2.6144215526286896, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.61)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
1.77528162361
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: None, reward: 0.588992679627
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.5889926796267861, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.59)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
1.18213715162
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: None, reward: 1.7643939253
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.7643939253022574, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.76)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, None)
2.11695021431
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: left, reward: 0.588926948505
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 0.58892694850488, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.59)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, 'forward')
1.58323990112
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: left, reward: 1.38126263544
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 3, 't': 17, 'action': 'left', 'reward': 1.3812626354387032, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.38)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 339
\-------------------------
Environment.reset(): Trial set up with start = (8, 5), destination = (7, 2), deadline = 20
0.275766716706
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2758; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None)
2.23091030564
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 2.81056514038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.8105651403822947, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.81)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
2.52073772301
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.26312106412
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.263121064119094, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.26)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'right')
2.05009812256
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.52717645253
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.5271764525284643, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.53)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.89192939357
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 2.70646391604
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.706463916038807, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.71)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
2.3931226015
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 2.05674136858
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.05674136857987, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.06)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward')
2.13960950569
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.76283313073
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.762833130732976, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.76)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.47326553846
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.82113595767
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.8211359576733406, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.82)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
2.14720074807
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 2.43117230723
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.431172307230318, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.43)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.47851096498
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.4785109649818868, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.48)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.35293858141
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 2.79742192602
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 2.797421926020121, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.80)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 1.49707815542
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.4970781554163834, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.50)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
2.07518025371
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 1.86573132544
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 1.8657313254447774, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.87)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', 'forward', 'right')
0.845048838176
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: right, reward: -0.110110990644
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': -0.11011099064399987, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove right instead of left. (rewarded -0.11)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.18232060635
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.1823206063506566, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent idled at a green light with oncoming traffic. (rewarded 1.18)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', 'forward')
1.62684682741
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: left, reward: 1.8956986088
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 1.895698608804, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.90)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
1.97045578958
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: 1.33437811376
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'left', 'reward': 1.3343781137607011, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.33)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: None, reward: 0.467766811346
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': None, 'reward': 0.46776681134614195, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.47)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
2.22493198504
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: forward, reward: 0.966346142462
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 0.9663461424615263, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.97)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 340
\-------------------------
Environment.reset(): Trial set up with start = (1, 6), destination = (5, 5), deadline = 25
0.274720791698
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2747; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'forward')
0.59709157752
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: right, reward: 0.328952611512
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.3289526115116741, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent drove right instead of left. (rewarded 0.33)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'right', 'left')
2.18885588187
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 1.59227243338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.5922724333812512, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 1.59)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 0.945887716951
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.9458877169509883, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.95)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'left')
1.38415438309
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 2.69002092323
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.690020923227967, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.69)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
1.48225126828
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 1.87045183827
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.8704518382726303, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.87)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: -40.3865694356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -40.38656943563049, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.39)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', None)
1.69241586886
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 2.4990519985
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.4990519984973094, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.50)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', None)
2.5735538663
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: 2.23493614641
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 2.2349361464146886, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.23)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.88384874632
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 1.77541686685
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.7754168668467312, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.78)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.65241695167
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: left, reward: 1.38762883656
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 1.387628836562273, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.39)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: forward, reward: 2.65208921791
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.6520892179120716, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.65)
56% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 341
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (8, 2), deadline = 20
0.273678833664
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2737; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.82963280658
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: None, reward: 2.46591508534
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.4659150853401948, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.47)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
2.14777394596
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: None, reward: 1.08290441019
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.082904410191639, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.08)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, 'left')
2.03708765316
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: None, reward: 2.74481300605
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.744813006050165, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.74)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
1.61533917808
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: None, reward: 2.62888800464
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.6288880046408973, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.63)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
1.52002289412
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: left, reward: 1.802914231
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 1.8029142310029305, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.80)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
2.21709384379
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: forward, reward: 1.38016427177
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.3801642717678102, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.38)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
2.24133172978
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.87659823174
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.8765982317356882, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.88)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: 1.81244663609
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.812446636091481, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.81)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'left')
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: left, reward: 1.43588931675
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 1.4358893167549662, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded 1.44)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
2.12211359136
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 0.966163211277
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 0.9661632112768237, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.97)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
2.06353542071
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.59207328205
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.5920732820531156, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.59)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', 'left', None)
1.82780435138
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.57200151846
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.5720015184575156, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.57)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', 'left')
1.46546637213
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.23466629124
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.2346662912411435, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.23)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: left, reward: 1.26910880444
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 1.269108804435974, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.27)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', 'forward')
1.29174402223
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 1.91688940942
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.9168894094209938, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.92)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
1.69990293492
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: None, reward: 1.97562394109
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.9756239410921441, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.98)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'left', None)
1.40487266163
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: left, reward: 0.696628019958
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 16, 'action': 'left', 'reward': 0.696628019958309, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.70)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
2.05896498076
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 0.79107100165
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 17, 'action': None, 'reward': 0.7910710016498992, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.79)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: right, reward: 0.494675632282
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 0.494675632281548, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded 0.49)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: left, reward: -9.34319288788
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': -9.343192887883555, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.34)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 342
\-------------------------
Environment.reset(): Trial set up with start = (1, 4), destination = (5, 6), deadline = 30
0.272640827557
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2726; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
1.99807788315
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 2.72162870788
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 2.721628707877819, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.72)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
2.09573393368
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.20917783978
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.2091778397797623, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.21)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
1.65245588673
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.19806052557
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.1980605255691708, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.20)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.92525820615
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.38518754419
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.3851875441853851, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.39)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', 'left')
1.50860391231
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.74729468633
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.7472946863307275, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.75)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
1.65522287517
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 0.997822302959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 0.997822302958977, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.00)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
2.40424500636
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 1.29831128574
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 1.2983112857377859, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.30)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.79862905778
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: forward, reward: 1.89392051257
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.893920512569905, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.89)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.4250179912
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: None, reward: 2.89242458193
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.892424581934736, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.89)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
1.84627478517
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: forward, reward: 1.69068809875
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 1.6906880987460933, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.69)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
1.63029843158
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 2.27993368295
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.279933682951235, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.28)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: right, reward: 1.06641351588
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 1.0664135158789736, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.07)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.55824847069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.5582484706898285, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.56)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'left', 'forward')
1.46374074777
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 2.26198444795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 17, 't': 13, 'action': None, 'reward': 2.261984447945212, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.26)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'left', None)
1.47086510188
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.23256668114
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.232566681143863, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.23)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', 'right')
1.78863728754
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.36940839952
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.3694083995232262, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.37)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: forward, reward: 2.16145871395
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 2.161458713950241, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.16)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', 'left', 'left')
1.58448069803
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: None, reward: 2.14275438207
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 13, 't': 17, 'action': None, 'reward': 2.1427543820680235, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.14)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: left, reward: -10.6189677192
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 18, 'action': 'left', 'reward': -10.618967719169527, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.62)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: left, reward: 0.952331188055
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 19, 'action': 'left', 'reward': 0.9523311880552864, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.95)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: left, reward: -10.7471167684
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 20, 'action': 'left', 'reward': -10.747116768362915, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.75)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: right, reward: 1.87880582199
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 1.8788058219944461, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.88)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, 'left')
2.00407320155
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 2.04807644077
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 2.0480764407738175, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.05)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', 'left', None)
1.83776343801
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 1.7787520406
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 7, 't': 23, 'action': None, 'reward': 1.7787520406013098, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.78)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: -39.5412510497
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 6, 't': 24, 'action': 'left', 'reward': -39.54125104971332, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.54)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 0.201253594961
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 5, 't': 25, 'action': None, 'reward': 0.20125359496141204, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.20)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: left, reward: 1.96009890816
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 4, 't': 26, 'action': 'left', 'reward': 1.9600989081552707, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.96)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 343
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (8, 7), deadline = 20
0.271606758388
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
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epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2716; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', None)
1.8082577393
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 2.59310579064
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.5931057906395845, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.59)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: forward, reward: -10.6160975977
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': -10.616097597662236, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.62)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 1.50629826981
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.5062982698078813, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.51)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
1.0507503408
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: left, reward: 2.36846896169
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 2.3684689616894743, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.37)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.76848144196
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: 2.48780701232
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.4878070123181186, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.49)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
2.15872128657
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.83818740119
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.8381874011878936, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.84)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
2.12814422714
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 1.39863534778
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.3986353477775402, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.40)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'forward', 'left')
1.37681431501
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 1.7530432864
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 1.753043286403333, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.75)
60% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 344
\-------------------------
Environment.reset(): Trial set up with start = (1, 3), destination = (5, 5), deadline = 30
0.270576611225
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2706; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', None)
2.35014024516
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 2.68096578889
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 30, 't': 0, 'action': None, 'reward': 2.6809657888929506, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.68)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'right', 'left')
0.447085192087
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 1.15862243161
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'left'), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 1.158622431607919, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'left')
Agent drove right instead of left. (rewarded 1.16)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.96992534166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.9699253416609472, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.97)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', 'right')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.62348655868
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.623486558682256, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.62)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: -10.6571460247
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': -10.65714602465219, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.66)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
1.64822396536
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.73507867906
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 2.73507867905991, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.74)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
1.85127814605
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 1.28786756825
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 1.2878675682472127, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.29)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'right', None)
1.77064875758
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: 1.47267181431
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.472671814307849, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.47)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'forward')
1.59335342263
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 2.50343349688
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.503433496876002, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.50)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'forward')
2.02838529684
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: forward, reward: 2.39739743977
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 2.397397439769324, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.40)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
1.87851132654
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 2.52927278685
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.529272786849905, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.53)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
1.24844209158
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: right, reward: 1.55610329562
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 1.55610329562219, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.56)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
2.49845434388
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: None, reward: 0.956453599537
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': 0.9564535995365029, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.96)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.76338978746
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: forward, reward: 1.6150932464
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 1.6150932463987209, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.62)
53% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 345
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (3, 5), deadline = 20
0.269550371193
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2696; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'forward')
2.34601401743
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.77736967181
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.7773696718079, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.78)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
2.2038920567
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.6973754855
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.697375485496103, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.70)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'right', 'right')
1.5247459745
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 1.29971279874
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'right'), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.299712798742081, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 1.30)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, 'right')
1.89494568443
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.26309809581
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.2630980958082467, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent followed the waypoint right. (rewarded 1.26)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 1.57697578958
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.5769757895789214, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.58)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
0.859409986288
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 1.88473693362
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.884736933616683, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.88)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', 'left', None)
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: left, reward: -9.02138820507
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -9.021388205074592, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.02)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'left', None)
1.50892961273
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 1.53807869676
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.5380786967641384, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.54)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', 'right', 'left')
1.41703514825
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: forward, reward: 0.0491664340027
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 0.04916643400265508, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent drove forward instead of right. (rewarded 0.05)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, 'forward')
2.56169184462
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 2.18168271197
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.1816827119688407, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.18)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
1.9506337711
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: 1.11129155167
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.111291551668796, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.11)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'right', None)
Environment.act() [POST]: location: (1, 2), heading: (-1, 0), action: None, reward: -4.73322054119
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 9, 't': 11, 'action': None, 'reward': -4.7332205411941946, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.73)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: right, reward: 1.94007164936
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.9400716493633487, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.94)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, 'left')
2.35985329552
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 2.50374504479
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 2.503745044793079, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.50)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'forward', 'left')
1.56492880071
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 1.40256257812
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 1.4025625781192665, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.40)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'right', 'left')
0.802853811847
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 1.21878285069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'left'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.218782850690988, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'left')
Agent drove right instead of left. (rewarded 1.22)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', None, 'left')
2.43179917015
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 2.3337509569
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 2.3337509569015626, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.33)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'right')
1.57902189012
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 2.23181619211
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 2.231816192110026, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent followed the waypoint right. (rewarded 2.23)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'forward')
2.2763996883
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 1.10286521932
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.102865219324731, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.10)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
1.54413840132
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 0.476187854249
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.47618785424917665, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.48)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 346
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (4, 2), deadline = 25
0.268528023474
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2685; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', 'left')
1.89056415763
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 1.82737190208
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 1.827371902080865, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 1.83)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: right, reward: 1.83114405052
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.8311440505177414, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 1.83)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: left, reward: -39.2729552897
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -39.272955289742214, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.27)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: None, reward: 0.486076056309
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 0.4860760563090418, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.49)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: None, reward: -4.58566315959
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': -4.585663159592102, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.59)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: forward, reward: -10.3035888778
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': -10.303588877848636, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.30)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'right')
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: left, reward: -39.9848698147
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -39.984869814650345, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.98)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: right, reward: 0.222275995309
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 0.22227599530861308, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.22)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
1.81078373536
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: 2.57861886765
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 2.578618867650004, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.58)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'left')
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: right, reward: 1.70941674436
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.7094167443636257, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove right instead of left. (rewarded 1.71)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'forward', None)
0.797174727631
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 1.23865180819
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.2386518081898954, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.24)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', None)
1.52350415475
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: 2.47665325026
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 2.4766532502551497, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.48)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'left')
1.90309128327
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: forward, reward: 2.7326889847
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 2.732688984696178, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.73)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
1.87854888885
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 2.47680579544
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 2.476805795442734, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.48)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'left', 'left')
2.10832526894
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 1.25176982551
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.251769825513828, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.25)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: None, reward: 1.15127365972
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.1512736597236186, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.15)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
1.43936381572
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: None, reward: 1.87779735053
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.8777973505298033, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.88)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, 'left')
2.31789013398
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: forward, reward: 1.45926237109
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.4592623710859265, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.46)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 347
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (4, 3), deadline = 30
0.267509553303
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2675; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'right')
1.71936729237
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.23318372914
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.233183729141359, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.23)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 1.87524459586
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 1.8752445958574975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded 1.88)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 1.68367703405
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 1.683677034047141, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.68)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'forward')
1.67635155328
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: left, reward: 2.91799438965
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 27, 't': 3, 'action': 'left', 'reward': 2.9179943896466254, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.92)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: -4.26076726787
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 26, 't': 4, 'action': None, 'reward': -4.260767267867942, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.26)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 1.65841846879
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.6584184687887285, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.66)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.01016312778
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 2.53164948114
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.531649481135447, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.53)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'right', None)
2.51555301702
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 1.05646684787
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.0564668478746324, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.06)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
2.20068176497
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: None, reward: 2.37953362095
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.3795336209460505, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.38)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
1.70960965124
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: left, reward: 1.5979936291
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 1.5979936291030203, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.60)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
2.17767734215
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 0.933905804151
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 0.9339058041513313, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.93)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None)
2.19165132221
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.44574281399
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 19, 't': 11, 'action': None, 'reward': 2.4457428139904485, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.45)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
2.3186970681
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.39601483625
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 12, 'action': None, 'reward': 2.396014836254198, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.40)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'forward', 'forward')
1.86699635823
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.67991057451
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 17, 't': 13, 'action': None, 'reward': 1.6799105745127163, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.68)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: -0.0699079715831
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 14, 'action': 'right', 'reward': -0.06990797158308171, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded -0.07)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, None)
1.77090630446
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 1.01772477213
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.0177247721329084, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.02)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', None, None)
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 1.50774659023
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 1.5077465902337566, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.51)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
1.67117217148
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: 2.16130038384
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 2.161300383838875, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.16)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, 'forward')
2.37168727829
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: None, reward: 1.76982044891
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 12, 't': 18, 'action': None, 'reward': 1.7698204489052556, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.77)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
1.91623627766
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 1.61642170352
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 19, 'action': 'right', 'reward': 1.6164217035170971, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.62)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: left, reward: 0.245654403102
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 10, 't': 20, 'action': 'left', 'reward': 0.24565440310243614, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove left instead of forward. (rewarded 0.25)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: left, reward: -39.4058352856
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 9, 't': 21, 'action': 'left', 'reward': -39.405835285632676, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.41)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, 'right')
1.90541904112
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: right, reward: 1.19977219818
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 8, 't': 22, 'action': 'right', 'reward': 1.1997721981817164, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent followed the waypoint right. (rewarded 1.20)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', None, 'forward')
2.04839345975
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 1.47086683465
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 7, 't': 23, 'action': None, 'reward': 1.4708668346454636, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.47)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', None, None)
1.65858058312
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: None, reward: 1.07213349162
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 6, 't': 24, 'action': None, 'reward': 1.0721334916205982, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.07)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'green', None, None)
1.68924151693
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 1.64771447334
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 25, 'action': 'forward', 'reward': 1.6477144733390012, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.65)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('right', 'green', None, 'forward')
1.68963245381
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: right, reward: 0.454994440381
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 4, 't': 26, 'action': 'right', 'reward': 0.4549944403811168, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 0.45)
10% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 348
\-------------------------
Environment.reset(): Trial set up with start = (3, 7), destination = (4, 4), deadline = 20
0.266494945976
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2665; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'forward')
1.60431671582
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 2.4858116054
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.48581160540186, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.49)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
2.29010769296
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: None, reward: 2.09638232923
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.0963823292254915, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.10)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', 'left')
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 0.954463088788
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 0.9544630887878819, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent drove right instead of left. (rewarded 0.95)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'forward')
2.0548198935
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 1.04633178281
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.046331782806114, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.05)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
1.3943155383
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.24190369901
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.2419036990070538, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.24)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.81810961865
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 1.59176999712
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.5917699971214772, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.59)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 1.5702985369
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.5702985368973466, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.57)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'forward')
1.30022162098
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: 0.213192182441
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 0.21319218244104388, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 0.21)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 1.25955634572
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.259556345723845, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded 1.26)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'left')
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: left, reward: 0.571997844214
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 0.5719978442139837, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove left instead of right. (rewarded 0.57)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
1.70493980789
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 2.74394213307
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.7439421330684906, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.74)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
2.22444097048
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 0.847338607145
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.847338607145331, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.85)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', 'right')
1.87487180899
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: None, reward: 1.84275060917
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.8427506091749095, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.84)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
1.01791326791
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: right, reward: 0.938400514244
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.9384005142435087, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.94)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
1.66847799513
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 0.684132520396
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.6841325203957302, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.68)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', 'right')
1.31174327934
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: None, reward: 1.68968466382
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 5, 't': 15, 'action': None, 'reward': 1.6896846638201137, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.69)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'left', None)
1.35171589151
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: None, reward: 1.64279271417
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.6427927141699208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.64)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
1.55579157315
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 0.582004733262
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 0.582004733262478, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.58)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', 'left', 'right')
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 2.07966846706
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': 2.0796684670610706, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 2.08)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', None, None)
2.1947013015
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: 1.73076769333
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 1, 't': 19, 'action': 'left', 'reward': 1.7307676933293668, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.73)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 349
\-------------------------
Environment.reset(): Trial set up with start = (8, 3), destination = (4, 5), deadline = 30
0.26548418684
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2655; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'right', 'forward')
2.12787612215
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 1.57031911543
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.5703191154313625, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.57)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'right')
1.33166493458
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 2.08762260544
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.0876226054393183, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.09)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'left')
1.93200411207
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 1.73268399732
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.7326839973230581, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.73)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'right', None)
2.30652462535
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 1.58216715563
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.5821671556276893, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.58)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.36535703737
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: 2.54832169032
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.5483216903244728, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.55)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'right')
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: None, reward: -4.19371555118
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 25, 't': 5, 'action': None, 'reward': -4.193715551178858, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.19)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
1.3000613355
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: left, reward: 0.829839833122
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 0.8298398331221047, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.83)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'right')
1.22901961638
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 1.37013640849
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 23, 't': 7, 'action': 'right', 'reward': 1.3701364084921985, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.37)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.95683936385
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 1.67566557031
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.6756655703128005, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.68)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.19739476397
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.197394763968164, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.20)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'left')
1.8323440547
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.02162470823
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.0216247082332828, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.02)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.17630525776
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: 1.14706314612
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.1470631461194147, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.15)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', 'forward')
1.86286259786
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: 1.98787224511
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.9878722451113242, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.99)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', 'forward')
1.95306858825
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: 1.80693621142
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 1.8069362114187315, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 1.81)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'left')
1.88857625253
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: forward, reward: 1.08408652991
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 14, 'action': 'forward', 'reward': 1.0840865299090547, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.08)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
1.96273449742
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: left, reward: 0.812376568514
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'left', 'reward': 0.8123765685140645, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.81)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, 'forward')
2.2128913683
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: forward, reward: 1.53281016706
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 1.5328101670614658, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.53)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
1.16168420194
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: forward, reward: 1.05977022848
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 1.0597702284761612, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.06)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 350
\-------------------------
Environment.reset(): Trial set up with start = (5, 5), destination = (3, 3), deadline = 20
0.2644772613
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2645; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'left')
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: -19.8418066512
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': -19.84180665115168, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.84)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'right')
1.63107657639
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: left, reward: 1.66376178349
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 1.6637617834899463, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 1.66)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
2.35735595218
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: None, reward: 2.74133893337
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.741338933370491, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.74)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: forward, reward: -10.2520319751
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.25203197507752, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.25)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
2.54934744277
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: None, reward: 1.16014965628
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.1601496562802276, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.16)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
1.56957285715
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: forward, reward: 2.07822057424
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.0782205742423834, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.08)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None)
2.0000787025
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: 2.84687478721
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 2.84687478720511, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.85)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
2.00682361552
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 2.64212082844
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.642120828440659, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.64)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
1.92698438147
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: None, reward: 1.35024722651
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.3502472265124665, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.35)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
1.08319704327
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: right, reward: 0.507762493063
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.5077624930629494, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.51)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
1.38755553297
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: left, reward: 0.848010664818
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': 0.8480106648176968, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.85)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'forward')
2.29717297146
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: left, reward: 2.26113677734
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 2.2611367773391073, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.26)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 351
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (8, 7), deadline = 20
0.263474154816
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2635; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
1.76632899059
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 2.85487156881
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.8548715688052946, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.85)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', 'left')
1.76336393068
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 1.54272538764
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 1.5427253876376033, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.54)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', 'left', 'forward')
1.65344595723
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: right, reward: 2.18574405835
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.1857440583464514, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 2.19)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'forward', 'forward')
1.88417735206
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 7), heading: (0, 1), action: forward, reward: 1.38048764737
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.3804876473718264, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 1.38)
80% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 352
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (2, 6), deadline = 20
0.262474852904
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2625; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'forward')
1.76127271811
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: left, reward: 2.24618188633
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 2.2461818863263865, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.25)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 2.55713077117
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.557130771170021, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.56)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
2.32447222198
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.97351493951
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.9735149395114775, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.97)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 1.05538199558
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.0553819955823223, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.06)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 1.47140716175
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.4714071617507818, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 1.47)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.53588978881
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.17803505736
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.178035057357525, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.18)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.35696242308
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 2.24466273218
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.244662732180509, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.24)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
2.2791548744
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: left, reward: 1.82403652674
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.824036526739533, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.82)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, 'forward')
1.0723134471
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 1.6085061948
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.6085061947986217, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.61)
55% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 353
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (6, 4), deadline = 20
0.261479341133
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2615; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'forward')
1.34040982095
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: 2.90710520385
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.9071052038471366, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.91)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', 'left', 'left')
2.35595657367
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: right, reward: 2.53288999051
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.5328899905126603, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.53)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: -4.75987572794
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': -4.75987572793828, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.76)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', 'forward')
1.88000239984
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: 1.95744250913
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.9574425091296161, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 1.96)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'forward')
1.7596301472
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 1.74981939372
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.749819393715055, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.75)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
1.11072721521
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: forward, reward: 1.91233088639
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.9123308863859505, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.91)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 354
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (3, 2), deadline = 25
0.260487605129
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2605; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.57370584534
Environment.act() [POST]: location: (8, 3), heading: (1, 0), action: right, reward: 2.45126132244
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 2.451261322441768, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 2.45)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: 1.95632986279
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.9563298627867614, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.96)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: forward, reward: -9.81236620135
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -9.812366201354237, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.81)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.49725430284
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.65076368229
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.650763682285101, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.65)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 0.998681722972
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 0.9986817229717627, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.00)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.06889815321
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 1.61392033761
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.6139203376079405, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.61)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'right')
1.70964377001
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.02788858311
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.0278885831097835, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.03)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.60218778816
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.57705529848
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.5770552984781978, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.58)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.58962154332
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.62494450339
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.6249445033898058, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.62)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.51685811102
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.5168581110199484, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.52)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'forward')
1.75472477046
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.27291504955
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.2729150495474069, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.27)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.5115290508
Environment.act() [POST]: location: (3, 3), heading: (1, 0), action: forward, reward: 2.30575799236
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 2.3057579923620066, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.31)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'left')
1.45096968166
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: left, reward: 1.99053038934
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 1.9905303893394033, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.99)
48% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 355
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (5, 5), deadline = 25
0.25949963057
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2595; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', None)
2.02213088333
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 1.36919459036
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.369194590356226, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.37)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
1.91459031523
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 1.41370754879
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.41370754879425, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.41)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
2.10572073065
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 1.82041634451
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 1.8204163445111847, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.82)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'left')
2.09787328758
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 2.17482842831
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.174828428305662, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.17)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'forward')
1.51381991
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 2.4862323963
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.486232396299105, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.49)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
1.90864352158
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: 2.36592601093
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.3659260109321005, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.37)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
2.13635085794
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 2.20141966699
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.2014196669896764, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.20)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.56207056719
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 1.20104194758
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.2010419475814156, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.20)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
1.85474854953
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 2.41004563503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.410045635028422, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.41)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
1.8238967157
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: forward, reward: 2.33079137287
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.330791372868748, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.33)
60% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 356
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (7, 7), deadline = 30
0.25851540319
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2585; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'right', 'forward')
2.14769924909
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.70855878664
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.7085587866389462, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.71)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'right', None)
1.25817158862
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: right, reward: 1.26769370219
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 1.2676937021920378, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.27)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'left')
1.86361754005
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.61884059881
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.6188405988112675, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.62)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.07400899256
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.05931969809
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.0593196980929351, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.06)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', 'right')
1.57902284353
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.68724364931
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.687243649311066, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.69)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.34140924541
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: 0.992798168392
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 0.9927981683915612, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.99)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
2.13728476626
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 1.23038093872
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 1.2303809387245876, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.23)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: -39.971345885
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': -39.97134588498903, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.97)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
1.38155625738
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 1.0081622515
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.0081622514964341, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.01)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'forward')
2.00002615315
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: None, reward: 2.12694836918
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 21, 't': 9, 'action': None, 'reward': 2.1269483691767785, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.13)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'right', None)
1.62166028594
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 0.902772690865
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 0.9027726908652249, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 0.90)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
1.11778309889
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 1.41642787821
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'left', 'reward': 1.4164278782117197, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.42)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'forward')
1.66414893201
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: 2.18932817674
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 2.18932817673561, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.19)
57% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 357
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (1, 7), deadline = 20
0.257534908777
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2575; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, None)
1.19485925444
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 1.36156733483
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.3615673348317, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.36)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
1.27821329464
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.25583843528
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.2558384352782754, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.26)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 1.6046670015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.6046670015010722, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.60)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
1.95511605726
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: None, reward: 1.94549712412
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.9454971241247334, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.95)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'forward', None)
0.978156891077
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 0.47002433981
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 0.47002433981007596, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.47)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', 'forward')
2.00372730222
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: left, reward: 2.57046397166
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 2.5704639716623436, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.57)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 1.09740524075
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.0974052407546961, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 1.10)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', None)
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: -9.9540307754
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -9.954030775398397, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.95)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: -40.5777026954
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -40.57770269538066, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.58)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'left', None)
1.65380164017
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: left, reward: 1.98690170079
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 1.986901700785843, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.99)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', 'left')
1.74122906943
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 1.52527884312
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.5252788431217983, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.53)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 0.202522162053
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 0.2025221620528601, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.20)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'left', None)
1.1671037069
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 2.33235187679
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 2.3323518767914013, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.33)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: 0.934389003869
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 0.9343890038690542, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.93)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
2.01248358389
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.79066768185
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.7906676818531595, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.79)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 358
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (7, 4), deadline = 25
0.256558133173
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2566; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: 0.800018489315
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 0.8000184893153868, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent drove forward instead of right. (rewarded 0.80)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
1.76702586496
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 1.66233065293
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.6623306529257904, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.66)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.71467825894
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 2.08860994904
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.0886099490379313, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.09)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.90164410399
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 1.78562480449
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.785624804493195, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.79)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.84363445424
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: 1.44180661987
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.4418066198670467, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.44)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: None, reward: -4.387733929
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': -4.387733929000728, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.39)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'left')
1.48633139122
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: forward, reward: 1.3655955327
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.3655955326957632, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.37)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.64272053705
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 2.07296452144
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.0729645214420054, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
2.16888526247
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 0.886053789983
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 17, 't': 8, 'action': None, 'reward': 0.8860537899825696, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.89)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.85784252925
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 2.65610499163
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.656104991629772, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.66)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
2.25697376044
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 1.11264137401
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.1126413740089944, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.11)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', 'forward')
1.91872245448
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: forward, reward: 2.35928767589
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 2.35928767588943, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.36)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'left')
1.42596346196
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: forward, reward: 2.29871020328
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 2.298710203279649, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.30)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'forward', 'left')
1.45717142844
Environment.act() [POST]: location: (7, 3), heading: (1, 0), action: None, reward: 2.16243905139
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.1624390513895513, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.16)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', None)
1.82001642286
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: right, reward: 1.79645165559
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 1.796451655585542, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.80)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 359
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (7, 6), deadline = 20
0.255585062272
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2556; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', 'left')
1.65304465916
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: 1.38603737139
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.3860373713859666, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.39)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: -9.18973718858
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -9.189737188583038, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.19)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: -9.842001596
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -9.842001596004796, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.84)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.68480756722
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: None, reward: 2.92504529618
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.925045296184071, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.93)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'right')
1.51974838378
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: forward, reward: 2.20407095144
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.2040709514428283, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 2.20)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
1.68383285249
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 1.5271728782
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.5271728781998797, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.53)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
1.82035167048
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: left, reward: 2.52562247178
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 2.5256224717802214, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 2.53)
65% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 360
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (5, 4), deadline = 30
0.254615682025
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2546; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: left, reward: 1.99152086597
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 1.9915208659708838, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.99)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
2.13239709228
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.41610471897
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.4161047189746805, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.42)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
1.77425090563
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.96001982207
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.9600198220728011, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.96)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.86713536385
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.49348042175
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.493480421745058, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.49)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
1.6803078928
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.81440104373
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.8144010437344995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.81)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 0.191980229232
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 0.1919802292324827, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.19)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', 'forward')
2.28709563694
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: 1.5390016247
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 24, 't': 6, 'action': 'left', 'reward': 1.5390016246982932, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 1.54)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, 'right')
1.86190966761
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 1.22769942029
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.2276994202917655, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 1.23)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: -9.44172639676
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': -9.441726396763945, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -9.44)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
1.74972779185
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 2.32480275584
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 2.324802755839161, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.32)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: right, reward: 1.50571381889
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 1.5057138188860226, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.51)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'left')
1.86233683262
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: forward, reward: 1.6683707321
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.66837073210131, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.67)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'left', None)
2.03726527384
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: forward, reward: 2.1553177774
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': 2.155317777403262, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.16)
57% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 361
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (5, 3), deadline = 20
0.253649978432
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2536; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.90157563287
Environment.act() [POST]: location: (3, 4), heading: (0, -1), action: right, reward: 1.25552550189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.2555255018872549, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.26)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', 'left')
1.68004754723
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: right, reward: 1.21771667502
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.2177166750199837, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.22)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'right')
1.50071397158
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 2.29454356695
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.2945435669512584, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.29)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 2.24098311246
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.2409831124619504, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.24)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
1.99416879036
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 1.74654602754
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.7465460275382914, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.75)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
2.07734404428
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: 1.36411150485
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.3641115048549033, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.36)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.80081257763
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 2.27427729219
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.274277292188504, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.27)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.26710548855
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: 1.00222170377
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.0022217037703285, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.00)
60% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 362
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (6, 7), deadline = 20
0.252687937549
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2527; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', 'left')
1.85896802985
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: forward, reward: 2.26113469358
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 2.261134693577473, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 2.26)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: left, reward: -10.7518327829
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': -10.75183278292419, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -10.75)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: None, reward: 1.65751905164
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.6575190516353806, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.66)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'forward', 'forward')
1.45943177804
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: forward, reward: 1.53660262536
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.5366026253600706, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 1.54)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 1.93369002369
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.9336900236866161, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.93)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', 'forward')
1.91959500779
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 1.10838818867
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.1083881886712277, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.11)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.34177143036
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.3417714303630883, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.34)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'right', None)
0.959735483754
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 0.657384715245
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 0.6573847152445822, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.66)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'forward')
1.92536742148
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 0.955363326841
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.9553633268412558, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 0.96)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
2.09629152562
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: forward, reward: 1.31992995152
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.3199299515235872, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.32)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
1.56666434533
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: None, reward: 1.70043576807
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.700435768072001, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.70)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', None)
1.6335500567
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: None, reward: 2.12149123934
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.121491239340543, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.12)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 3), heading: (0, -1), action: left, reward: -10.7138272875
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': -10.713827287490245, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.71)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
1.70811073857
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: forward, reward: 0.630772080695
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.6307720806948818, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.63)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
1.60550286535
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: forward, reward: 1.55397662478
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 1.5539766247846132, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.55)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 363
\-------------------------
Environment.reset(): Trial set up with start = (4, 5), destination = (7, 7), deadline = 25
0.251729545485
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2517; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', None)
1.16944140963
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: forward, reward: 2.616834522
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 2.616834522003047, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.62)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
1.92673855438
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: forward, reward: 2.22276631433
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 2.22276631432945, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.22)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'forward', None)
1.72072777457
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: 2.73934804084
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 2.7393480408367594, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.74)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
1.96306853758
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 1.70035004747
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.700350047471446, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.70)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 7), heading: (0, 1), action: forward, reward: 2.31512414195
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.3151241419471225, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.32)
80% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 364
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (4, 6), deadline = 25
0.250774788399
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2508; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'forward')
1.51399159823
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 1.83127837699
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.831278376993751, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.83)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
1.87752064802
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 2.67522223298
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.675222232976979, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.68)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
2.2763714405
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 1.39081618287
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.3908161828738579, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.39)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.83359381169
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 2.75718922675
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.7571892267520397, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.76)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 1.67982066652
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.6798206665156639, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.68)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
1.80391282116
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 1.44073525279
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.4407352527859478, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.44)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', 'right')
0.728925778425
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: 1.39887493433
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.3988749343310483, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove forward instead of left. (rewarded 1.40)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', 'right')
0.521297306324
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: forward, reward: 0.47062797828
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 0.4706279782797902, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 0.47)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'right')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 1.13915671204
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.1391567120426163, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove right instead of left. (rewarded 1.14)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, 'left')
1.7207500355
Environment.act() [POST]: location: (8, 5), heading: (0, 1), action: left, reward: 1.26140749841
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 16, 't': 9, 'action': 'left', 'reward': 1.2614074984087464, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.26)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
1.25436124774
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: right, reward: 1.38718484508
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 1.3871848450830186, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.39)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: -5.92670120178
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 14, 't': 11, 'action': None, 'reward': -5.926701201781005, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.93)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'forward')
2.07475243435
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 2.74069078084
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 2.7406907808380754, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.74)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, 'forward')
2.06348726116
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 2.54723202383
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.5472320238331854, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.55)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'left')
1.52746952622
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.34850919107
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.3485091910666545, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.35)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
2.29539151922
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.80697385822
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.8069738582233261, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.81)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', None)
1.89313796582
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 0.801436326477
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': 0.8014363264765894, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.80)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'left', None)
1.34728714615
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: forward, reward: 1.96825315756
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 1.96825315755501, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.97)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'right', None)
1.78600993245
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: None, reward: 1.2962636595
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.2962636594993875, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.30)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: None, reward: 1.96441138709
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.9644113870948925, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.96)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
1.68965818264
Environment.act() [POST]: location: (4, 5), heading: (-1, 0), action: None, reward: 2.40082741968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': 2.4008274196827317, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.40)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, 'forward')
2.05159570057
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 6), heading: (0, 1), action: left, reward: 1.76268001359
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 1.762680013587297, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.76)
12% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 365
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (1, 4), deadline = 20
0.249823652506
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2498; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: -4.03114849823
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': -4.031148498233167, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.03)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
1.32077304641
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 0.524900174066
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 0.5249001740657083, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.52)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', 'right')
2.16657091834
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 2.0940140152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'right'), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 2.0940140152043307, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'right')
Agent followed the waypoint right. (rewarded 2.09)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'left')
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 1.77446100764
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.7744610076394567, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded 1.77)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
2.04524280116
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 2.35718079267
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.3571807926739883, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.36)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
1.13466359616
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: left, reward: 2.09308848486
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 2.0930884848594133, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.09)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, 'left')
1.49107876695
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: left, reward: 2.16372697337
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 2.1637269733715954, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.16)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
1.57855056738
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 1.12385230933
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.123852309331585, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.12)
60% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 366
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (1, 4), deadline = 20
0.248876124071
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
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epsilon = 0.2489; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2489; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
2.20121179692
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 2.19313668645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.193136686446306, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.19)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'right', 'right')
1.73789676307
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 2.23492254282
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'right'), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.2349225428205313, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 2.23)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
2.19717424168
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 2.52818638585
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.528186385854215, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.53)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: right, reward: -20.8247992581
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': -20.824799258102868, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.82)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -10.3859788427
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.385978842731646, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.39)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
2.19324501109
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.88038091327
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.8803809132717573, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.88)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.44639397593
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.4463939759318793, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 1.45)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'right', 'right')
0.784494369442
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 0.0920887754247
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'right'), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 0.09208877542466576, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'right')
Agent drove forward instead of left. (rewarded 0.09)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: right, reward: 1.31745723985
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.3174572398494426, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.32)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
2.3106002797
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: right, reward: 1.81607275525
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.8160727552547933, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.82)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
2.05118268872
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: None, reward: 2.34671214948
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.346712149479327, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.35)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', None)
2.1989474191
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: None, reward: 2.6812725382
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.6812725381976135, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.68)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
2.44010997865
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: None, reward: 0.951752461942
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.9517524619415119, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.95)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
1.65777015185
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: 1.13712940585
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.137129405846818, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.14)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'forward', None)
2.2300379077
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 1.1247410749
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 1.1247410749027424, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.12)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', 'left', None)
2.42347674485
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 0.522600426489
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.5226004264894852, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 0.52)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'forward')
2.3053596425
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 1.26192394705
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.2619239470487342, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.26)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', 'left', None)
1.6959312203
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 0.694399938871
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 3, 't': 17, 'action': None, 'reward': 0.6943999388707744, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.69)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: forward, reward: -10.9233601726
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': 'forward', 'reward': -10.923360172569254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.92)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', None)
1.23776579143
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: -0.65834700608
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': -0.6583470060801268, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded -0.66)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 367
\-------------------------
Environment.reset(): Trial set up with start = (1, 3), destination = (8, 6), deadline = 20
0.247932189411
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2479; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, 'left')
1.76535378236
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 1.39822082591
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.3982208259052826, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.40)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 2.06906589626
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.069065896263387, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.07)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.19516557958
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 2.1490728544
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.149072854395608, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.15)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.67211921699
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 2.87126298968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.8712629896769752, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.87)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
2.27169110333
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.61659997832
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.6165999783182627, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.62)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
1.94414554083
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.49051463391
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.490514633910507, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.49)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
1.39744977885
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: 2.81249400621
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.812494006210134, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.81)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
1.78364179477
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.32485154925
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.3248515492524082, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.32)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
2.3049264317
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.70161167099
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.701611670994445, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.70)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
2.00326905135
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.06752096274
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.067520962735167, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.94743194351
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: forward, reward: 0.911858281226
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 0.9118582812259417, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.91)
45% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 368
\-------------------------
Environment.reset(): Trial set up with start = (5, 3), destination = (3, 5), deadline = 20
0.246991834896
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2470; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
2.36268031377
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.36245449111
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.3624544911052794, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.36)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
1.62232403698
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 1.66152165551
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.6615216555057053, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.66)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: None, reward: 2.45976035135
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.4597603513511332, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.46)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: right, reward: 0.607355097867
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 0.6073550978670968, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 0.61)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, 'left')
2.38277506353
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: right, reward: 1.87649638856
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.876496388563556, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.88)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
1.47303858567
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: right, reward: 2.51962669585
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.519626695848034, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.52)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
2.10497189253
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 2.27245139491
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.272451394908515, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.27)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
2.03539500704
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.99142466688
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.991424666882115, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.99)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
1.43798935865
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.04507186173
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.0450718617287342, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.05)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'forward')
2.4077216076
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: 0.829691999444
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 0.8296919994443763, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.83)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'right', None)
1.54113679597
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 2.4495776015
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.4495776015042408, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.45)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'left')
2.39095032961
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 1.07107389349
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.0710738934865989, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.07)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, 'right')
2.41780384341
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 0.838426641969
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 8, 't': 12, 'action': None, 'reward': 0.8384266419686186, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 0.84)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
0.724090615443
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: right, reward: 0.486664707327
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.4866647073274214, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.49)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
2.06333651748
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: right, reward: 1.3046371938
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.3046371937952792, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.30)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: right, reward: 0.926248765233
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.9262487652329954, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 0.93)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: None, reward: -4.14289116551
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': -4.142891165509219, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.14)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'forward')
2.0707538636
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: None, reward: 1.94985890492
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.9498589049156732, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.95)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
1.53096266138
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: None, reward: 1.71667614359
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.7166761435885238, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.72)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'right', None)
1.26293264541
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: right, reward: 1.06929254529
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 1, 't': 19, 'action': 'right', 'reward': 1.0692925452858926, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.07)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 369
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (8, 5), deadline = 20
0.246055046948
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2461; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'left')
1.35006633169
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 2.21646658792
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.216466587917514, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.22)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
2.03681296218
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 1.34313373667
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.343133736666695, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.34)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.68997334942
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 2.6033281285
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.6033281284980223, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.60)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
2.08225396855
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: left, reward: 1.14473216586
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.1447321658561713, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.14)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'left')
1.82740287016
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: left, reward: 2.53712123373
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 2.5371212337320133, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.54)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'left')
1.58178730413
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: 2.83007414622
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.8300741462243386, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.83)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'right', None)
1.94434589049
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.31110673762
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.3111067376219827, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.31)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
1.87035740895
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 1.17022214061
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.170222140605621, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.17)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
1.52028977478
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.75401143638
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.754011436376075, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.75)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
1.6773894913
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 1.02205046285
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.0220504628490703, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.02)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 370
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (3, 6), deadline = 30
0.245121812039
Simulating trial. . .
epsilon = 0.2451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2451; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2451; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
1.90713785708
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 2.44743861182
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 2.447438611820994, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.45)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
2.01340983696
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.88262492614
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.882624926142363, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.88)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'right')
1.36876617656
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.86922906783
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.8692290678266545, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.87)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.94801738155
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.4838623521
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.4838623520950085, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.48)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
2.21593986682
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.12618007596
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.1261800759578713, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.13)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
2.17105997139
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.24283840207
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.2428384020746404, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.24)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'forward')
1.61870680352
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 2.49872119887
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 2.4987211988726883, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.50)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.70694918673
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.66877144087
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 7, 'action': None, 'reward': 2.668771440869945, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.67)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
1.71733008737
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.22912543005
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.229125430046867, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.23)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', 'right')
1.79746238764
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 0.967174917812
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'right'), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 0.9671749178116233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'right')
Agent followed the waypoint forward. (rewarded 0.97)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'right', None)
2.12772631406
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.84307239183
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 20, 't': 10, 'action': None, 'reward': 1.8430723918344065, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.84)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
2.1878603138
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.47155308649
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 11, 'action': None, 'reward': 2.4715530864877246, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.47)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: -10.8111644527
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 12, 'action': 'forward', 'reward': -10.811164452662082, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.81)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: right, reward: 0.095991984397
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 0.09599198439704804, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent drove right instead of forward. (rewarded 0.10)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', None)
2.14665073896
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 1.20920757048
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.2092075704801426, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.21)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: None, reward: 0.792853647356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 15, 'action': None, 'reward': 0.7928536473555161, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.79)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
1.38101210049
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: right, reward: 0.560206431393
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 0.560206431393097, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 0.56)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', None, None)
1.61387604051
Environment.act() [POST]: location: (1, 6), heading: (0, 1), action: left, reward: 2.63962436703
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 17, 'action': 'left', 'reward': 2.6396243670348207, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.64)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'green', None, None)
2.12675020377
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: left, reward: 2.42446558501
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 18, 'action': 'left', 'reward': 2.424465585012605, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.42)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, 'forward')
1.55424667201
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 2.24096244051
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 11, 't': 19, 'action': None, 'reward': 2.2409624405137114, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.24)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
2.32970670014
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: None, reward: 2.57238496554
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 10, 't': 20, 'action': None, 'reward': 2.5723849655444386, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.57)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
2.18871164372
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 0.832131475141
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 0.8321314751411752, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.83)
27% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 371
\-------------------------
Environment.reset(): Trial set up with start = (4, 6), destination = (8, 5), deadline = 25
0.244192116693
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2442; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: 0.522064944812
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 0.5220649448115617, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.52)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, 'left')
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: right, reward: 1.88709322701
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.8870932270068141, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.89)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
2.0587140012
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: forward, reward: 1.78063271782
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 1.7806327178191514, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.78)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
1.91967335951
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 2.45265412979
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 2.452654129794535, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.45)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.42964511237
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 1.57810629658
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.5781062965816397, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.58)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
1.68398685564
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.06039256706
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.0603925670550998, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.06)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 0.551668489437
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 0.5516684894370593, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 0.55)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.86256740244
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.76551683428
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.7655168342839174, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.77)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'right')
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: forward, reward: -10.8092539611
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': -10.809253961076681, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent attempted driving forward through a red light. (rewarded -10.81)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
1.81404211836
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 1.32235911535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.3223591153511278, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.32)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
1.56820061686
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: None, reward: 0.963596313774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 0.9635963137741785, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.96)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
2.27560789439
Environment.act() [POST]: location: (1, 5), heading: (0, -1), action: left, reward: 2.43314657862
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 2.4331465786175963, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.43)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
2.35437723651
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: left, reward: 1.38593766924
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 1.3859376692363332, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.39)
48% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 372
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (5, 3), deadline = 25
0.243265947486
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2433; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.62381940249
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.14853139552
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.1485313955227396, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.15)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
1.95038759439
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 1.14629512761
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 1.1462951276081312, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.15)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, 'left')
1.95658402408
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: right, reward: 2.92908757751
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 2.9290875775120053, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.93)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
2.45104583284
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 1.4543534122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.4543534122024044, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.45)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.95269962252
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 2.28726408557
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.2872640855729616, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.29)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, 'forward')
2.18616374465
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: forward, reward: 2.38613686369
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.386136863686449, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.39)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
2.11998185405
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 2.45400816101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.454008161008268, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.45)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
2.28699500753
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 1.54193890133
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.5419389013277338, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.54)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
2.13715060558
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 2.33594876862
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.335948768620808, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.34)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'right', 'forward')
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: right, reward: 0.0374951749768
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.03749517497680421, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent drove right instead of forward. (rewarded 0.04)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: None, reward: -4.19488202839
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': -4.19488202839486, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.19)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, 'forward')
2.17728823445
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: left, reward: 2.05984514598
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 2.0598451459834735, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.06)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: -19.3185470034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': -19.31854700337601, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.32)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
1.6134930672
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: left, reward: 1.10186909265
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 1.101869092645274, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.10)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
1.50387570447
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: forward, reward: 1.84241327871
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 1.8424132787083412, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.84)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 373
\-------------------------
Environment.reset(): Trial set up with start = (3, 6), destination = (6, 4), deadline = 25
0.242343291043
Simulating trial. . .
epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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epsilon = 0.2423; alpha = 0.5000
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/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'right', None)
1.16611259535
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: right, reward: 1.7658752998
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.765875299796397, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 1.77)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
1.97322775871
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 2.68164579121
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.681645791207836, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.68)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
2.32743677496
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 2.36828749827
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.3682874982656648, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.37)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
1.51042155943
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: forward, reward: 2.39209285525
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 2.392092855252812, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.39)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: left, reward: 0.691321724428
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 0.6913217244279971, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.69)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: None, reward: -5.26447331545
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': -5.264473315445468, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.26)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'forward')
1.52500313882
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: right, reward: 1.23003285422
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.2300328542156636, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.23)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, 'forward')
1.75749361262
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 2.1169664537
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.1169664537006057, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.12)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: left, reward: -10.7213674199
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': -10.721367419914037, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -10.72)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
1.26589846531
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 2.44252167647
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.442521676470076, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.44)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', None, 'right')
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: right, reward: 0.985188754714
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 0.9851887547140373, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent drove right instead of left. (rewarded 0.99)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
1.37218971135
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 1.75944761304
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.7594476130409222, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.76)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'right', 'forward')
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 0.70670415421
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 0.7067041542100201, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent drove forward instead of right. (rewarded 0.71)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, 'forward')
2.01030638426
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 2.67603382762
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.6760338276226143, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.68)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: left, reward: -39.6454161546
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': -39.645416154585845, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.65)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None)
1.56581866219
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 1.95336701863
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 1.95336701862541, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.95)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'right')
1.29957801244
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 2.11559514884
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 2.1155951488442923, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 2.12)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.37241073681
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 1.3724107368132077, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.37)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
1.85421007089
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 0.724249006738
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 0.724249006737878, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.72)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', None)
1.23539140104
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 0.900402989545
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 6, 't': 19, 'action': None, 'reward': 0.9004029895449506, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.90)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'left', None)
1.06789719529
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.35419320591
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 5, 't': 20, 'action': None, 'reward': 2.354193205910321, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.35)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'left', None)
1.35768107992
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: left, reward: 1.21827355859
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': 'left', 'reward': 1.2182735585932314, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.22)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
1.87015745287
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: left, reward: 0.546437964219
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 0.5464379642194448, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.55)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'red', 'forward', None)
2.2365496871
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 0.56236702476
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.5623670247601611, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.56)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', 'forward', None)
1.39945835593
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 0.904160993153
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 1, 't': 24, 'action': None, 'reward': 0.9041609931526158, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.90)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 374
\-------------------------
Environment.reset(): Trial set up with start = (2, 2), destination = (5, 6), deadline = 25
0.241424134041
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2414; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'forward')
1.63233249972
Environment.act() [POST]: location: (3, 2), heading: (1, 0), action: forward, reward: 2.00708346927
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 2.0070834692739057, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 2.01)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: left, reward: 1.9717984445
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 1.971798444499922, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.97)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
1.548341361
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: 2.31776716965
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 2.317767169651387, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.32)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.91446695443
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 2.04827649177
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.0482764917699847, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.05)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.9813717231
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 2.30420085388
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.3042008538755017, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.30)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: -5.87375069855
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': -5.873750698550514, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.87)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'left')
2.20593072518
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: 2.32262939644
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 2.322629396441518, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.32)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: -4.02606364015
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': -4.026063640154056, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.03)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', 'forward', None)
0.605377661385
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: right, reward: -0.0657028613342
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': -0.06570286133420289, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded -0.07)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'forward', None)
1.69566273684
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: right, reward: 2.73891717709
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 2.7389171770921954, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.74)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: forward, reward: -10.786839724
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': -10.786839723980673, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.79)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'left', None)
1.93305426533
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: right, reward: 1.97112258336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.9711225833569799, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.97)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'forward', 'forward')
1.34645477311
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 2.59114507153
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 2.59114507152751, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 2.59)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, 'forward')
1.93723003316
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 2.10824688474
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.1082468847389695, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.11)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'left', 'right')
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: -9.27907481598
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': -9.27907481598068, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent attempted driving forward through a red light. (rewarded -9.28)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', 'left', None)
1.28797731926
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: left, reward: 0.776509731835
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 0.7765097318352827, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.78)
36% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 375
\-------------------------
Environment.reset(): Trial set up with start = (5, 3), destination = (2, 5), deadline = 25
0.240508463208
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2405; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward')
1.77345346637
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.00234304814
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.002343048137075, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.00)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
1.15180967454
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 2.14167781154
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.141677811543638, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.14)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
1.64674374304
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.83838317674
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.838383176737366, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.84)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'right', 'forward')
1.84909761879
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.37401821999
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.3740182199917608, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.37)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', 'right')
1.89762876927
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.36780083138
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.3678008313822623, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.37)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', None)
1.74256345989
Environment.act() [POST]: location: (5, 3), heading: (-1, 0), action: None, reward: 1.71654661599
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.7165466159894631, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.72)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: forward, reward: 1.81244446334
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.812444463338302, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.81)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.67314449159
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: forward, reward: 1.4578248413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.4578248413016257, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.46)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
2.28615030417
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: 1.85799422731
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 1.857994227312922, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.86)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', None)
1.7110452006
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: 0.865353538178
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 0.865353538178111, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.87)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', 'right')
1.56611094003
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: None, reward: 2.49971606885
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.499716068850337, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.50)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', None)
1.03224352555
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: left, reward: 1.02633960481
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 1.0263396048123918, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 1.03)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.56548466645
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 5), heading: (0, 1), action: forward, reward: 2.45531935426
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 2.455319354255964, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.46)
48% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 376
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (5, 5), deadline = 30
0.239596265322
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
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epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2396; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.28922953882
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.33104586951
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.3310458695135665, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.33)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.31013770416
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.56850297173
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.568502971726588, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.57)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
2.0508415988
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.14111712068
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.1411171206804775, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.14)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.5727116564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.5727116563991457, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.57)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.77163195605
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.7716319560534537, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.77)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', 'forward')
1.87515919529
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.52898846118
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.5289884611766922, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.53)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', 'right')
1.06390035638
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: 1.31043658671
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'right'), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 1.3104365867066927, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'right')
Agent drove forward instead of left. (rewarded 1.31)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'left')
2.18226205195
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: 1.11555474499
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 1.1155547449894447, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.12)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'forward')
2.11856669022
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: 2.32082848279
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 22, 't': 8, 'action': 'left', 'reward': 2.320828482794689, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.32)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: -5.67602385441
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 21, 't': 9, 'action': None, 'reward': -5.67602385440542, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -5.68)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', 'forward')
1.38789825725
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.69187237254
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.6918723725390956, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.69)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'forward', None)
1.72955503794
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: None, reward: 2.76456987327
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 19, 't': 11, 'action': None, 'reward': 2.764569873272321, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.76)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: left, reward: -9.95812217554
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 12, 'action': 'left', 'reward': -9.958122175535536, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.96)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
1.58108222021
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 2.2125991566
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 2.212599156602278, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.21)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
2.14278628849
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.06179057393
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.061790573932262, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.06)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
1.60228843121
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.55394746667
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 15, 'action': None, 'reward': 2.5539474666680215, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.55)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: -10.0483005982
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': -10.048300598153263, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.05)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', 'left')
1.48374568941
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 2.09317321955
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 2.093173219552549, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 2.09)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', None, 'right')
1.54480454395
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 2.40054599805
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 12, 't': 18, 'action': 'forward', 'reward': 2.4005459980454593, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 2.40)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', None, 'right')
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: right, reward: 2.58580778632
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 11, 't': 19, 'action': 'right', 'reward': 2.585807786315431, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent followed the waypoint right. (rewarded 2.59)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'forward', None)
2.24706245561
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 1.20706067299
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 20, 'action': None, 'reward': 1.2070606729931417, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.21)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'forward', 'forward')
1.8197079845
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: forward, reward: 1.22957274113
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 9, 't': 21, 'action': 'forward', 'reward': 1.2295727411306145, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 1.23)
27% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 377
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (2, 4), deadline = 25
0.23868752721
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2387; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, 'right')
1.61899762219
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 1.97719741139
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.9771974113900646, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.98)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'forward')
1.89760455626
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 1.22676495893
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.2267649589250027, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.23)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
2.07811794894
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 2.20117352652
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.201173526523638, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.20)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'left')
1.74153061019
Environment.act() [POST]: location: (6, 5), heading: (1, 0), action: None, reward: 1.30012690465
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.3001269046521104, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.30)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
2.01040201035
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: 0.969643964378
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 0.9696439643781067, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.97)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
1.52082875742
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.09427020988
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.0942702098765817, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.09)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: forward, reward: -40.0614957034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': -40.061495703432506, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.06)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
2.13964573773
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 2.35182117468
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.3518211746784203, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.35)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'forward')
1.56218475759
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: None, reward: 1.7294769527
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.7294769526997844, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.73)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
1.8968406884
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: forward, reward: 2.55361592308
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.5536159230781963, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.55)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', 'forward')
1.44036537416
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: None, reward: 0.909490336708
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 15, 't': 10, 'action': None, 'reward': 0.9094903367076719, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 0.91)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', None)
2.34786213661
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: None, reward: 2.60082951684
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.600829516843251, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.60)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (1, 0), action: left, reward: -9.71561790024
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': -9.715617900239154, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.72)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', 'forward')
2.13900506519
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: 2.00075219811
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 2.0007521981086254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.00)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: None, reward: -5.22392637396
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 11, 't': 14, 'action': None, 'reward': -5.223926373963785, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.22)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, 'left')
2.26428006081
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: forward, reward: 1.72227725798
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 10, 't': 15, 'action': 'forward', 'reward': 1.722277257977847, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.72)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', None, 'left')
1.64890839847
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: left, reward: 1.97875684942
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 1.9787568494180097, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.98)
32% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 378
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (6, 6), deadline = 25
0.237782235749
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2378; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: forward, reward: -40.4115291432
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': -40.411529143165914, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.41)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'right', None)
1.99535719874
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 2.75312591238
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.753125912375552, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.75)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.93932033795
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.99862329188
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.9986232918834521, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.00)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.27054251372
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.270542513717917, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.27)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: left, reward: -9.21628979821
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': -9.216289798208198, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.22)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'forward', None)
1.66176562289
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: None, reward: 1.30659570026
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.3065957002583615, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.31)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: right, reward: 1.26583357721
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.2658335772093903, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.27)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'left')
1.81383262394
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: 1.71243677967
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 1.7124367796673323, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.71)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
1.20829770855
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: 1.93772140519
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 1.9377214051889435, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.94)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
2.47434582673
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.04807585791
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.0480758579090583, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.05)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: left, reward: -10.3669168216
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': 'left', 'reward': -10.36691682162972, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -10.37)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', 'forward')
1.17492785544
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.69724209512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.697242095121157, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.70)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
2.26121084232
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.84766963786
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.847669637861091, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.85)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
1.95125720734
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 1.87620949609
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.87620949608617, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.88)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'right', 'forward')
1.69815756002
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 0.770153204095
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 0.770153204095267, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 0.77)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: left, reward: 1.43922672124
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 1.439226721242437, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.44)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, None)
1.75959284041
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: right, reward: 1.22495226829
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 1.2249522682935963, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.22)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', 'left')
1.26665019549
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 0.375059460259
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'left'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 0.37505946025895853, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'left')
Agent drove forward instead of right. (rewarded 0.38)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, 'right')
1.70758658064
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 2.31318129382
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 2.3131812938249974, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 2.31)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, 'forward')
1.37751799652
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: right, reward: 1.41769202711
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.4176920271104554, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.42)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, None)
1.96897181491
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: None, reward: 0.811010349072
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': 0.8110103490721152, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.81)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: forward, reward: 0.251720818243
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.25172081824272474, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove forward instead of left. (rewarded 0.25)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: 0.0953712501871
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.09537125018707004, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.10)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', None, 'left')
1.73101211155
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.67981441948
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 2, 't': 23, 'action': None, 'reward': 1.6798144194766138, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.68)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', None, None)
1.38999108199
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.57556254167
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 1, 't': 24, 'action': None, 'reward': 1.5755625416727923, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.58)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 379
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (5, 6), deadline = 20
0.236880377869
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2369; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None)
2.05444024009
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: None, reward: 2.22468065577
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.2246806557746455, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.22)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 3), heading: (0, -1), action: right, reward: 1.4461481512
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.4461481512028183, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.45)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', 'forward', 'forward')
1.4980172017
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: forward, reward: 0.560917528079
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 0.5609175280789055, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 0.56)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
1.48418066157
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 1.85770153892
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.8577015389184401, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.86)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 1.31267804183
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.3126780418300483, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.31)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, 'left')
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: left, reward: 1.78042106198
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.7804210619780052, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.78)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
1.57300955687
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: 1.58963123543
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 1.589631235426678, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.59)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.49002298736
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 2.75851766563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 2.758517665625425, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.76)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: left, reward: -40.8683191155
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -40.86831911549949, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -40.87)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 1.16059913835
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.1605991383482503, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.16)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 1.13046366539
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.1304636653904412, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove forward instead of right. (rewarded 1.13)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, 'right')
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: -4.63442641381
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 9, 't': 11, 'action': None, 'reward': -4.6344264138120765, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent idled at a green light with no oncoming traffic. (rewarded -4.63)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
1.49227255435
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: right, reward: 0.871617361149
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 0.8716173611494535, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.87)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: forward, reward: -40.510517455
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': -40.5105174549726, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.51)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
1.886175399
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 2.33476890336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 2.334768903362618, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.33)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None)
1.18194495775
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 0.73847188273
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 0.7384718827299499, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.74)
20% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 380
\-------------------------
Environment.reset(): Trial set up with start = (7, 4), destination = (4, 5), deadline = 20
0.235981940545
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2360; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'forward')
2.39254895439
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: 2.29690684367
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.296906843670281, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.30)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
1.95208842434
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: right, reward: 1.05764417345
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.0576441734473563, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.06)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'left')
Environment.act() [POST]: location: (6, 4), heading: (-1, 0), action: None, reward: 2.0569981017
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.0569981016987327, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.06)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: forward, reward: 1.57119981619
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.5711998161946799, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.57)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
2.2457334562
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 2.82981982489
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.829819824893036, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.83)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 2.4944724907
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.4944724907019005, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.49)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', 'right')
0.495962642302
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: 0.211108293822
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'right'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 0.21110829382236473, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'right')
Agent drove forward instead of left. (rewarded 0.21)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', None)
1.67094110025
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 1.93816571267
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.9381657126676122, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.94)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', 'left')
1.51666854858
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 1.56423203575
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.564232035752724, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.56)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: right, reward: 0.508236835963
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.5082368359627156, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.51)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'left')
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: right, reward: 1.39942160323
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.3994216032253661, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.40)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'left', 'forward')
1.67263498761
Environment.act() [POST]: location: (4, 4), heading: (0, 1), action: right, reward: 1.34682771912
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'forward'), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 1.346827719119986, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'forward')
Agent followed the waypoint right. (rewarded 1.35)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'right', 'left')
2.06005136172
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 5), heading: (0, 1), action: forward, reward: 2.04194137374
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 2.041941373740114, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 2.04)
35% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 381
\-------------------------
Environment.reset(): Trial set up with start = (8, 5), destination = (3, 3), deadline = 25
0.235086910804
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2351; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'forward')
1.52464036281
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: forward, reward: 2.7606896816
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 2.7606896815969417, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 2.76)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
2.3093714086
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: forward, reward: 2.43609924978
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 2.4360992497783016, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.44)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'right')
2.13313324642
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: None, reward: 2.94962822379
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.949628223789228, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.95)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.13956044793
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: None, reward: 2.56123867418
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.5612386741823423, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.56)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: None, reward: 2.598120725
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.598120725001574, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.60)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.74246658395
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: forward, reward: 2.2751864994
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.275186499396435, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.28)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: 1.90875553655
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.908755536553116, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.91)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
1.7270615643
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 2.78841444055
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.788414440553817, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.79)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', 'right')
0.935076702538
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 1.52055919444
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.520559194440198, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 1.52)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', 'right')
1.19528671844
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: forward, reward: 2.114134456
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 2.114134456001996, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent followed the waypoint forward. (rewarded 2.11)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 1.08684450044
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 15, 't': 10, 'action': 'left', 'reward': 1.0868445004410447, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent drove left instead of forward. (rewarded 1.09)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', 'forward', None)
1.80823403922
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 1.11997013933
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 1.1199701393275028, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.12)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
0.96020842024
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 0.780114388159
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 0.780114388159397, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.78)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: left, reward: -19.5090049143
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': -19.50900491427415, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -19.51)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', None, None)
1.58132039615
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: 1.87792653811
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'left', 'reward': 1.8779265381111374, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.88)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 382
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (3, 2), deadline = 20
0.234195275723
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2342; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: left, reward: 1.32391974924
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 1.3239197492437764, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.32)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
2.47426014303
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.37606699053
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.376066990533001, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.38)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.92516356678
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.71680909889
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.716809098891875, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.72)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.32098633284
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.84553453035
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.84553453035379, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.85)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: left, reward: -9.93418774742
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': -9.934187747416802, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.93)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', 'forward')
1.43608497528
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.79514786931
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.7951478693125789, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.80)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
2.00882654168
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: 1.20039478924
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.2003947892445364, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.20)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
2.37273532919
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: 0.896488602632
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 0.8964886026318373, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.90)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, None)
2.53777664055
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: None, reward: 2.32188516923
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.3218851692258937, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.32)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: 0.0525628831932
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 0.05256288319316482, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.05)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: -9.27242127821
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': 'left', 'reward': -9.272421278214455, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving left through a red light. (rewarded -9.27)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'forward', None)
2.21728995697
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 0.814744561351
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.8147445613506097, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 0.81)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, None)
2.11047215118
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 2.52851241629
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.5285124162913144, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.53)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
0.8701614042
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 1.21221763178
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.212217631784931, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.21)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 383
\-------------------------
Environment.reset(): Trial set up with start = (8, 5), destination = (5, 4), deadline = 20
0.233307022425
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2333; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'forward')
0.463022094516
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: right, reward: 1.35528105122
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.355281051215909, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'forward')
Agent drove right instead of left. (rewarded 1.36)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
1.04118951799
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: right, reward: 2.91187248681
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.9118724868065966, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.91)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'right', None)
1.98539935294
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 2.14390848805
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.1439084880533423, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.14)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, 'right')
1.79809751679
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: None, reward: 1.48232694389
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.482326943890086, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.48)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.60461066546
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: 2.70047889701
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.700478897011903, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.70)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
2.42983090489
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 2.67428368359
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.6742836835851307, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.67)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
2.55205729424
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.06307320428
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.0630732042760256, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.06)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: -9.64173458893
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': -9.641734588927209, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.64)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
1.80754948365
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.86803633433
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.8680363343276372, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.87)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'left')
1.99327865939
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.18555981861
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.1855598186142404, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.19)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
2.31949228374
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 0.793722572113
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 0.7937225721127688, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.79)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
1.55660742793
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 2.36495529841
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.3649552984074607, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.36)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, 'forward')
2.34317010594
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 2.19993087659
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.1999308765881387, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.20)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: -4.08333321547
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': -4.083333215474193, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.08)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, None)
1.9765310024
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: right, reward: 1.10739462408
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.1073946240806696, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.11)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'left', 'forward')
2.06987863165
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: forward, reward: 1.7472567343
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 1.7472567343035716, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 1.75)
20% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 384
\-------------------------
Environment.reset(): Trial set up with start = (6, 3), destination = (3, 5), deadline = 25
0.232422138085
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2324; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', None)
0.45973102495
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: forward, reward: 1.14133077091
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 1.1413307709104434, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.14)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'left')
1.77177788189
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: left, reward: 1.82559377721
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 1.8255937772065205, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.83)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
2.5832604316
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: 2.60982229647
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.609822296466235, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.61)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', 'left')
1.51954101527
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: forward, reward: 1.59254621636
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 1.5925462163600572, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.59)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
1.63461196591
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: 2.31385010554
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.313850105542946, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.31)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
1.54196281324
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: right, reward: 2.41221017078
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 2.412210170778204, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.41)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'right', None)
2.0646539205
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: None, reward: 1.315285158
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.3152851580038347, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.32)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.97423103573
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: forward, reward: 1.40982934119
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 1.4098293411861997, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.41)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: -4.16341564032
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 17, 't': 8, 'action': None, 'reward': -4.16341564031694, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.16)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'forward')
1.64583085515
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 2.46836963404
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.4683696340405215, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.47)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'right', None)
1.68996953925
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 2.20133925409
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.201339254090347, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.20)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: left, reward: -9.26818140404
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': -9.268181404038103, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.27)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', 'right')
2.54138073511
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 2.76841302685
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 13, 't': 12, 'action': None, 'reward': 2.7684130268522287, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.77)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: right, reward: 0.838806603454
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 0.838806603453967, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 0.84)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'right', None)
2.37424155556
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 1.29382700663
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.2938270066289546, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.29)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (0, 1), action: right, reward: 1.13749958468
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 15, 'action': 'right', 'reward': 1.1374995846765958, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.14)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'forward')
1.39760501181
Environment.act() [POST]: location: (3, 7), heading: (-1, 0), action: right, reward: 2.35566194969
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 2.3556619496855014, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.36)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, 'forward')
1.87663348075
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: right, reward: 2.25989171372
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 2.2598917137172316, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 2.26)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, None)
1.80756524926
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 1.75636646646
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.7563664664576122, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.76)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', None, 'forward')
2.05710024459
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 1.35985166482
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 6, 't': 19, 'action': None, 'reward': 1.3598516648203212, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.36)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 2.10801474807
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 5, 't': 20, 'action': None, 'reward': 2.1080147480737907, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.11)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: None, reward: 0.52086766975
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 4, 't': 21, 'action': None, 'reward': 0.5208676697504436, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.52)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'green', None, None)
1.69203018846
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: forward, reward: 0.698710883081
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': 0.6987108830808257, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.70)
8% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 385
\-------------------------
Environment.reset(): Trial set up with start = (4, 5), destination = (1, 3), deadline = 25
0.231540609925
Simulating trial. . .
epsilon = 0.2315; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2315; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2315; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2315; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2315; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2315; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2315; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2315; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2315; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: -5.54857783983
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': -5.548577839829298, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.55)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
1.72962346713
Environment.act() [POST]: location: (3, 5), heading: (-1, 0), action: left, reward: 1.43761448782
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'left', 'reward': 1.4376144878248656, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.44)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: 1.34850713841
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 1.3485071384141867, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.35)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
2.25773800243
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.74780094038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.747800940379827, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.75)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
2.5027694714
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.83932070582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.8393207058220147, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.84)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: -20.6714200439
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 20, 't': 5, 'action': 'left', 'reward': -20.67142004392467, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.67)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
1.19537053577
Environment.act() [POST]: location: (1, 5), heading: (-1, 0), action: forward, reward: 1.71622874103
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 1.7162287410262467, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.72)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', None, 'right')
2.01038393723
Environment.act() [POST]: location: (1, 4), heading: (0, -1), action: right, reward: 2.57098942578
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 2.570989425780565, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 2.57)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'forward')
2.07207226574
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: forward, reward: 2.84915573328
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 17, 't': 8, 'action': 'forward', 'reward': 2.8491557332814814, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.85)
64% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 386
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (8, 5), deadline = 20
0.230662425215
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2307; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'left')
1.54045029216
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 2.27803763806
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.2780376380564786, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.28)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', 'right')
1.85881120908
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 2.2084842515
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.208484251501038, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.21)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
1.80455340646
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: None, reward: 1.01703816355
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.017038163546453, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.02)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'forward', None)
0.80053089793
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 0.595858199078
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 0.5958581990783739, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.60)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: -4.77170817124
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': None, 'reward': -4.771708171237235, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -4.77)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'left')
1.83779290899
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.0865785231
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.0865785230994989, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.09)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 0.757534353625
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 0.7575343536245644, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 0.76)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.48277681183
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: None, reward: 2.00218184645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.002181846445814, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.00)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.74247932914
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: None, reward: 0.981699918684
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 0.9816999186836641, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.98)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.58361897748
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: left, reward: 2.09379672705
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'left', 'reward': 2.0937967270480367, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.09)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', 'left')
1.55604361582
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: forward, reward: 2.42036629417
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 2.4203662941703827, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.42)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.4557996384
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: forward, reward: 2.65048272566
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 2.650482725659791, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.65)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (-1, 0), action: forward, reward: 0.955820266072
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 0.9558202660722876, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.96)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 0.839764273483
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 0.8397642734827587, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.84)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'right', 'left')
2.05099636773
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: 0.629803827797
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.6298038277973668, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent followed the waypoint forward. (rewarded 0.63)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
1.50448072405
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: forward, reward: 1.79492542338
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': 1.794925423380985, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.79)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'forward')
1.70847595471
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 2.40250549878
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': 2.4025054987783827, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.40)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: forward, reward: -40.9239995966
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': -40.92399959655956, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.92)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'left', None)
2.59654136403
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 0.958530039554
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.9585300395540328, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.96)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'left', 'forward')
1.86181558518
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 0.798272793039
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.7982727930389375, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 0.80)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 387
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (3, 3), deadline = 20
0.229787571274
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2298; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'forward', 'forward')
1.70207382824
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 2.19749075082
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.1974907508214514, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.20)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
1.410795785
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: None, reward: 1.57160755052
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.5716075505207703, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.57)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: forward, reward: -9.96994129225
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -9.969941292246926, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -9.97)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'right', None)
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: forward, reward: -9.24240592524
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -9.242405925241723, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent attempted driving forward through a red light. (rewarded -9.24)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: forward, reward: 0.240559019521
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 0.24055901952113745, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 0.24)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.36208962391
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: 2.30402376572
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.304023765718154, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.30)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'right', None)
1.83403428109
Environment.act() [POST]: location: (7, 4), heading: (0, 1), action: None, reward: 1.53598115588
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.5359811558793517, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.54)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.83870785226
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: left, reward: 1.46611965895
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.4661196589508532, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.47)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
1.64970307372
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 1.93579449722
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 1.9357944972236338, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.94)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
1.79274878547
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 2.46548795476
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 2.4654879547584856, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.47)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'right', None)
1.94565439667
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.18313277004
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.1831327700368033, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.18)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
1.78196585786
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.7816681662
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.7816681661988734, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.78)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'right', None)
2.06439358335
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.32819703773
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.328197037733235, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.33)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
2.12911837011
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: 0.805676337854
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 0.805676337854281, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.81)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'green', 'left', None)
1.02929156518
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: left, reward: 0.556964626533
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 6, 't': 14, 'action': 'left', 'reward': 0.5569646265332111, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent followed the waypoint left. (rewarded 0.56)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 388
\-------------------------
Environment.reset(): Trial set up with start = (1, 3), destination = (4, 5), deadline = 25
0.22891603547
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2289; alpha = 0.5000
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epsilon = 0.2289; alpha = 0.5000
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Simulating trial. . .
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/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward')
2.0398853149
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.74939362795
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.7493936279453273, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.75)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', None)
2.17104508861
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.30386925142
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.303869251420652, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.30)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: left, reward: -39.7708493837
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 23, 't': 2, 'action': 'left', 'reward': -39.77084938370002, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.77)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
2.23745717002
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 1.19452513472
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.1945251347175179, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.19)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'forward', None)
1.69291372204
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 2.5687676248
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.5687676247987223, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.57)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 0.461783172924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 0.4617831729240808, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 0.46)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'right', None)
0.808560099499
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 0.649032634751
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 0.6490326347509229, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.65)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.65241375561
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: left, reward: 2.87272586306
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 2.8727258630610826, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.87)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 1.1562354939
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.1562354938958346, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent drove right instead of left. (rewarded 1.16)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, None)
1.97708649201
Environment.act() [POST]: location: (8, 4), heading: (0, -1), action: right, reward: 0.941271588561
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.9412715885605194, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.94)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None)
1.50486629889
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: right, reward: 2.789171034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': 'right', 'reward': 2.789171034000277, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.79)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
1.78181701203
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 2.75313756289
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 11, 'action': None, 'reward': 2.753137562894869, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.75)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
2.26747728746
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: None, reward: 1.67630732353
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.6763073235271615, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.68)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.46739735398
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 2.63286473259
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 2.6328647325904715, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.63)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'right', 'left')
1.11388745233
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.1320795116
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 11, 't': 14, 'action': None, 'reward': 1.132079511600625, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent properly idled at a red light. (rewarded 1.13)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
1.71599115237
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 1.43239355194
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.4323935519367337, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.43)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'forward', None)
1.57419235215
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: None, reward: 2.03408817052
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 2.03408817051819, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.03)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', 'right')
1.65471058722
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: 0.854269629866
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 0.8542696298660002, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent followed the waypoint forward. (rewarded 0.85)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: -5.39227101164
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 7, 't': 18, 'action': None, 'reward': -5.3922710116404495, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.39)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: -4.67074619848
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 6, 't': 19, 'action': None, 'reward': -4.670746198475282, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.67)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: left, reward: -0.375735077833
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 20, 'action': 'left', 'reward': -0.3757350778330828, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded -0.38)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('right', 'green', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: forward, reward: -0.551189126206
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': -0.5511891262056062, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent drove forward instead of right. (rewarded -0.55)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', None, 'forward')
2.06826259723
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: right, reward: 0.97835260588
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 3, 't': 22, 'action': 'right', 'reward': 0.9783526058804457, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 0.98)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', None, None)
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: 0.434934401769
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 23, 'action': None, 'reward': 0.4349344017688448, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.43)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'forward', None)
1.49120166776
Environment.act() [POST]: location: (4, 2), heading: (1, 0), action: None, reward: 2.03673194539
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 1, 't': 24, 'action': None, 'reward': 2.03673194539061, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.04)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 389
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (1, 3), deadline = 20
0.228047805217
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2280; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'forward', 'left')
0.940127742975
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 1.35419018052
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.3541901805170145, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 1.35)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
2.26256980933
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: 2.89712827339
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 2.8971282733872252, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.90)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'green', None, 'forward')
1.77180866787
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: left, reward: 2.11211800712
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': 2.1121180071197774, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.11)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: -5.35627824159
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': None, 'reward': -5.356278241590332, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent idled at a green light with no oncoming traffic. (rewarded -5.36)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
1.46896318871
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 2.11196058087
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.11196058087212, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.11)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
2.05013104329
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: 2.70414782519
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.704147825193516, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.70)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'forward')
2.27155049126
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.66964404388
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.669644043883425, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.67)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
1.96078136317
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 1.14862683179
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.1486268317942299, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.15)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.45115417706
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.4511541770648613, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.45)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None)
2.00292913727
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 0.836421944861
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 0.8364219448612193, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.84)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'right', 'left')
2.03263327997
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 1.15181204992
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'left'), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.1518120499222808, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'left')
Agent followed the waypoint right. (rewarded 1.15)
45% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 390
\-------------------------
Environment.reset(): Trial set up with start = (5, 7), destination = (1, 3), deadline = 30
0.227182867979
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2272; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, 'right')
1.64741917994
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: left, reward: 2.7575258187
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 30, 't': 0, 'action': 'left', 'reward': 2.757525818696566, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.76)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: None, reward: 1.69497380639
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.694973806388949, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.69)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'right')
2.65489688098
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: None, reward: 1.36579982967
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.3657998296659208, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.37)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
2.15254478124
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: forward, reward: 1.68864842339
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 1.6886484233856818, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.69)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'forward')
2.46061399951
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: forward, reward: 2.46803686165
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 2.468036861654464, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.47)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, None)
1.97189230549
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.1850030092
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.185003009200493, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.19)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'right', None)
2.19629531054
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.87017373629
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 24, 't': 6, 'action': None, 'reward': 1.8701737362923392, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.87)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'right', 'left')
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: left, reward: -20.3865627111
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', 'left'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': -20.38656271111852, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'left')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.39)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, 'left')
1.79046188479
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 2.51847684321
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 22, 't': 8, 'action': 'forward', 'reward': 2.5184768432146836, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.52)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'forward', None)
1.51601725916
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 1.88867629069
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 1.8886762906936674, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.89)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'forward')
2.46432543058
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: forward, reward: 1.14468237232
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 1.1446823723237975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.14)
63% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 391
\-------------------------
Environment.reset(): Trial set up with start = (3, 7), destination = (6, 5), deadline = 25
0.226321211265
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2263; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', None)
1.92059660231
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 2.90666951161
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 2.906669511605445, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.91)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
1.73625475409
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.95640670727
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.9564067072673246, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.96)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.84633073068
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.12970877952
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.1297087795234342, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.13)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
2.41363305696
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: 2.00851479064
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 2.008514790636349, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.01)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: right, reward: 0.586448855908
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.5864488559077535, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent drove right instead of forward. (rewarded 0.59)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'right', None)
1.68500771849
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 2.66320470477
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.6632047047653415, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.66)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.20416367175
Environment.act() [POST]: location: (4, 2), heading: (-1, 0), action: right, reward: 0.859829073939
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 0.8598290739394533, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 0.86)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'green', 'forward', None)
1.70234677493
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: right, reward: 2.2949931105
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 2.294993110503735, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.29)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
1.41967554107
Environment.act() [POST]: location: (4, 7), heading: (0, -1), action: None, reward: 2.21211434245
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.2121143424462013, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.21)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'forward')
1.52330760156
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: right, reward: 1.15932651529
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 1.1593265152889445, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.16)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'left')
2.154469364
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 1.17398158493
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.1739815849325852, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.17)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: -9.40594679307
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': -9.405946793071084, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.41)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: None, reward: 1.8212010485
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.8212010485049583, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.82)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
2.57984904136
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: left, reward: 2.07787637098
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 2.077876370981776, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.08)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', None)
1.80414026134
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 2.59274353555
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 11, 't': 14, 'action': None, 'reward': 2.592743535550487, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.59)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'forward', None)
2.19844189844
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 0.992912136663
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 0.9929121366631941, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.99)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'forward', None)
1.59567701755
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 1.05339613616
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.053396136158076, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.05)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', 'right')
1.25449010854
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: forward, reward: 0.890812360891
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 8, 't': 17, 'action': 'forward', 'reward': 0.8908123608905139, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent followed the waypoint forward. (rewarded 0.89)
28% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 392
\-------------------------
Environment.reset(): Trial set up with start = (3, 7), destination = (8, 5), deadline = 25
0.225462822634
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2255; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'forward')
2.02273845895
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 2.38903542755
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 25, 't': 0, 'action': None, 'reward': 2.3890354275474004, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.39)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: left, reward: -39.5364936261
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 24, 't': 1, 'action': 'left', 'reward': -39.53649362610199, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.54)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: -10.6065632471
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': -10.606563247128133, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -10.61)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', None)
1.76396680658
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 2.92814037508
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.9281403750787502, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.93)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'forward', None)
0.614400695738
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 0.677799635353
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 0.6777996353525869, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.68)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: right, reward: 1.84473108564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.8447310856380081, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.84)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
1.46218571604
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: None, reward: 2.01735900101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.017359001014847, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.02)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 1.41692635452
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 18, 't': 7, 'action': 'right', 'reward': 1.4169263545196173, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded 1.42)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
2.34605359083
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 2.47873732827
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.4787373282696556, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.48)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', 'left')
1.90924396511
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 2.35448722212
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.354487222121694, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.35)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
2.41239545955
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 1.27304313358
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.2730431335829318, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.27)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
0.646100165546
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 0.331629912646
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 0.3316299126457928, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.33)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, 'left')
1.79868582955
Environment.act() [POST]: location: (3, 6), heading: (0, -1), action: left, reward: 0.839338427491
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 0.8393384274910671, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 0.84)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'forward')
1.9419633375
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: left, reward: 2.53267139729
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 2.5326713972933286, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.53)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, None)
2.37713943424
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: forward, reward: 1.86819441155
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 1.8681944115513736, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.87)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', 'left', None)
1.4880197551
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 2.00516314802
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 2.0051631480242937, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.01)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', 'left', 'forward')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: 1.27838719182
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 9, 't': 16, 'action': 'left', 'reward': 1.2783871918184855, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent drove left instead of forward. (rewarded 1.28)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', 'left', 'forward')
2.34472789903
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 2.2961867688
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'forward'), 'deadline': 8, 't': 17, 'action': None, 'reward': 2.2961867688048354, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.30)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', None)
1.99587487604
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: right, reward: 1.44304790369
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.4430479036921078, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.44)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
1.45917904028
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: right, reward: 1.13616400746
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.136164007458636, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.14)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, None)
1.57844765735
Environment.act() [POST]: location: (8, 6), heading: (0, -1), action: None, reward: 1.47490473373
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.4749047337317782, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.47)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, None)
2.1226669229
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: forward, reward: 0.91031570191
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 0.9103157019103922, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.91)
12% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 393
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (2, 3), deadline = 25
0.224607689689
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2246; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'right', 'left')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 0.19583825917
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 0.1958382591698622, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent drove forward instead of right. (rewarded 0.20)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
1.71946138987
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 2.01336013336
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': 'right', 'reward': 2.0133601333604165, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.01)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
1.81589494176
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 2.01203957224
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.012039572240533, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.01)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, None)
1.29767152387
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 1.62047431614
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 1.6204743161365271, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.62)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'right')
1.972675271
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: forward, reward: 1.71591513283
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.715915132833628, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 1.72)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: -5.96814874719
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': -5.968148747193467, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.97)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'left')
1.73977235853
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.11372938374
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.1137293837441025, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.11)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'left')
1.92675087114
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.09684102084
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.096841020839295, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.10)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
2.01179594599
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.83484040991
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.8348404099148254, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.83)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.52667619554
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 2.25294748254
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.252947482542723, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.25)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
1.88981183904
Environment.act() [POST]: location: (8, 4), heading: (1, 0), action: None, reward: 1.07151040217
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.0715104021725088, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.07)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (1, 0), action: forward, reward: 1.68591113123
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 1.6859111312252226, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.69)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, 'left')
1.66422547447
Environment.act() [POST]: location: (2, 4), heading: (1, 0), action: forward, reward: 1.217102055
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 1.2171020550030616, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.22)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'right')
2.20247249932
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 3), heading: (0, -1), action: left, reward: 0.796877626914
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 12, 't': 13, 'action': 'left', 'reward': 0.7968776269138205, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 0.80)
44% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 394
\-------------------------
Environment.reset(): Trial set up with start = (5, 4), destination = (7, 2), deadline = 20
0.223755800084
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2238; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', None)
0.728796367125
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: right, reward: 0.772674239711
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 0.7726742397108317, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.77)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
1.913967257
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.35777866265
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.357778662648829, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.36)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'left', None)
1.86641076161
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 1.203727
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.2037270000028775, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.20)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
1.53506888081
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: right, reward: 2.24798031122
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 2.247980311220849, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.25)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.48066112061
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 1.12206896771
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.1220689677090914, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.12)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
1.74659145156
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 2.34742929941
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.3474292994078825, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.35)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
2.2110739238
Environment.act() [POST]: location: (6, 3), heading: (1, 0), action: forward, reward: 2.75879095739
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.758790957393953, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.76)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: left, reward: 1.16401986496
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 1.164019864963652, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.16)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', 'right')
1.57869603162
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 1.33695933026
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.336959330264941, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.34)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'forward', None)
1.99866994272
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: right, reward: 1.45636078178
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 1.4563607817798476, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.46)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 395
\-------------------------
Environment.reset(): Trial set up with start = (4, 4), destination = (3, 7), deadline = 20
0.222907141516
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2229; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.4775982984
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 1.76771621561
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.7677162156052135, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.77)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
1.84271929657
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 1.36191046648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.3619104664773543, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.36)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
1.60231488152
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 2.34689685784
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.3468968578391274, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.35)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'forward', 'left')
2.13186559362
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 2.10223276179
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.102232761791336, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.10)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', None)
1.97460586968
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 0.955591260792
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 0.9555912607920805, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.96)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: 1.81101813129
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.8110181312941247, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded 1.81)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, None)
1.622657257
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 1.74206886355
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.7420688635531822, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.74)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', 'left', 'forward')
1.91304863082
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: 2.77486673603
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.7748667360258326, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.77)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, 'left')
1.31901212852
Environment.act() [POST]: location: (4, 3), heading: (-1, 0), action: left, reward: 0.882064719111
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 12, 't': 8, 'action': 'left', 'reward': 0.8820647191109121, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 0.88)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
2.4849324406
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: forward, reward: 1.47973495937
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.4797349593739766, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.48)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, 'forward')
2.47059726757
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 2.27068465582
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.2706846558199922, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.27)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, None)
1.63587295982
Environment.act() [POST]: location: (3, 3), heading: (-1, 0), action: None, reward: 2.24176097542
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 9, 't': 11, 'action': None, 'reward': 2.2417609754179475, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.24)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', 'left')
1.44888211112
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: right, reward: 1.56118176562
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.5611817656182512, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.56)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: None, reward: -4.47249688174
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': -4.472496881738893, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.47)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', None, 'right')
1.84429520192
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 7), heading: (0, -1), action: forward, reward: 2.50172714294
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 2.5017271429431718, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 2.50)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 396
\-------------------------
Environment.reset(): Trial set up with start = (2, 6), destination = (1, 3), deadline = 20
0.222061701731
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2221; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', 'forward')
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: 2.80270296732
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 2.8027029673239086, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'forward')
Agent followed the waypoint left. (rewarded 2.80)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, None)
2.32886270617
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: left, reward: 2.4648987563
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 2.464898756303194, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.46)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (1, 7), heading: (0, 1), action: None, reward: 0.901061467484
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 18, 't': 2, 'action': None, 'reward': 0.9010614674835621, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded 0.90)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: forward, reward: 1.92293359396
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 1.922933593956252, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.92)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
2.04701037549
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 2.54920740355
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.5492074035533685, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.55)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
2.29810888952
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 1.9860010475
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.9860010474979617, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.99)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 1.28894841259
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.288948412587491, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded 1.29)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: right, reward: 1.12733590035
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.127335900346933, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent drove right instead of left. (rewarded 1.13)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', 'right')
1.45782768094
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.41766763698
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.417667636978177, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.42)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'forward', None)
1.46410208928
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 0.805148722695
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.8051487226949012, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 0.81)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'left', None)
1.89152459601
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: right, reward: 1.93391246686
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.9339124668584051, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.93)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'left')
1.92331817795
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 1.52208078409
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.5220807840927983, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.52)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
1.30136504416
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 2.17436093101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.1743609310077243, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.17)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, None)
1.73786298758
Environment.act() [POST]: location: (1, 2), heading: (0, 1), action: None, reward: 1.70530034044
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 1.705300340440657, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.71)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 1.41382216067
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 1.4138221606686807, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded 1.41)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, 'left')
1.29997459357
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: -0.063971765612
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 5, 't': 15, 'action': 'forward', 'reward': -0.06397176561201312, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent drove forward instead of left. (rewarded -0.06)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', 'forward')
2.04506416061
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.57180445452
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 4, 't': 16, 'action': None, 'reward': 1.5718044545237682, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.57)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', None)
1.28819936939
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.5761826876
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 3, 't': 17, 'action': None, 'reward': 1.5761826876025398, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.58)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', None)
1.4321910285
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 1.63015999915
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.63015999914971, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.63)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', 'left', None)
1.53117551382
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: None, reward: 0.748294355492
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.7482943554919257, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.75)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 397
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (2, 2), deadline = 20
0.221219468521
Simulating trial. . .
epsilon = 0.2212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2212; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2212; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'right', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: -4.22998124032
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 20, 't': 0, 'action': None, 'reward': -4.229981240320321, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.23)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'green', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: 1.24308358404
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.243083584037182, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.24)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
1.35107825202
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 1.87739639741
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 18, 't': 2, 'action': 'right', 'reward': 1.8773963974062364, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.88)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 2), heading: (0, -1), action: left, reward: 1.69333098272
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'left', 'reward': 1.6933309827207528, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.69)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', None, 'forward')
1.34131705842
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: right, reward: 1.55272235331
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.5527223533148744, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent followed the waypoint right. (rewarded 1.55)
75% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 398
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (8, 5), deadline = 30
0.220380429724
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2204; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'left', 'right')
2.03291350444
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.57414888163
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.5741488816255516, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.57)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', 'right')
1.80353119303
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.51193796233
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'right'), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.5119379623276297, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.51)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.13973493466
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 2.80304176641
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.803041766407782, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.80)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', 'left', None)
0.979802438634
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 0.317494694533
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 0.31749469453338397, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.32)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: 1.34253966747
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 26, 't': 4, 'action': 'left', 'reward': 1.3425396674665382, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.34)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, 'forward')
2.20588694325
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 1.86695242705
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.8669524270517805, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.87)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', 'right', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: -39.1754589584
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 24, 't': 6, 'action': 'left', 'reward': -39.17545895843867, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent attempted driving left through a red light with traffic and cause a major accident. (rewarded -39.18)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
1.86971019935
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: left, reward: 0.929556239972
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 0.9295562399716006, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.93)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
2.14205496851
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: None, reward: 1.03237137731
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.0323713773130956, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.03)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
1.98233369998
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: forward, reward: 1.45264110261
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 1.4526411026118733, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.45)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'right', None)
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: left, reward: -20.1100098592
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 20, 't': 10, 'action': 'left', 'reward': -20.11000985918997, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.11)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'right', None)
2.03323452342
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: 2.45023944898
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 19, 't': 11, 'action': None, 'reward': 2.450239448982853, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.45)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'forward', None)
1.32453657686
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: None, reward: 2.27889202559
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 12, 'action': None, 'reward': 2.2788920255910083, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.28)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
2.13084067342
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: forward, reward: 2.00048458717
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 2.000484587167999, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.00)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: left, reward: -9.46866176807
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 14, 'action': 'left', 'reward': -9.46866176806922, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving left through a red light. (rewarded -9.47)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
1.72158166401
Environment.act() [POST]: location: (1, 6), heading: (-1, 0), action: None, reward: 2.67967614231
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 15, 'action': None, 'reward': 2.6796761423130326, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.68)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'green', None, None)
1.60120122181
Environment.act() [POST]: location: (8, 6), heading: (-1, 0), action: forward, reward: 0.82348897359
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 16, 'action': 'forward', 'reward': 0.8234889735895583, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.82)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', None, None)
1.61423732471
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: right, reward: 0.879238373443
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 13, 't': 17, 'action': 'right', 'reward': 0.879238373443302, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.88)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 399
\-------------------------
Environment.reset(): Trial set up with start = (1, 7), destination = (5, 6), deadline = 25
0.219544573225
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2195; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'right', 'left')
0.944137184383
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 1.01493853943
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 1.0149385394322925, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', 'left')
Agent drove forward instead of left. (rewarded 1.01)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.2123450977
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 1.75539819796
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.7553981979569933, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.76)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.58721317291
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 0.988156083931
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 0.9881560839311356, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.99)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.28768462842
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 2.87293177564
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.8729317756380057, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.87)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.7174874013
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 1.91125694387
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.911256943871991, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.91)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
2.08030820203
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 2.6380949826
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.6380949825954527, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.64)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
2.35920159231
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 2.53661120436
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.5366112043617113, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.54)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: 0.918099869194
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 18, 't': 7, 'action': 'left', 'reward': 0.9180998691935607, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.92)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'green', None, None)
1.24673784908
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: right, reward: 1.25009667138
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': 1.2500966713817272, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.25)
64% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 400
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (5, 3), deadline = 25
0.218711886952
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2187; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
1.68236306028
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.84448902214
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.8444890221418497, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.84)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
1.76342604121
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.59661710194
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.5966171019371331, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.60)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.68002157157
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.01775548623
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.0177554862348617, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.02)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'forward')
2.03641968515
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.24179045107
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.2417904510689108, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.24)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
1.39963321966
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: left, reward: 1.57508135239
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.575081352389957, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.58)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', 'left')
1.84512602899
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.53799214968
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.5379921496821876, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 2.54)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
2.44790639834
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.22013092416
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.2201309241551206, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.22)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', 'left')
2.19155908933
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 1.97589427566
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.9758942756621172, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.98)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
1.83401866125
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: None, reward: 2.31378233923
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.3137823392289567, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.31)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 0.864467803152
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': 0.8644678031520276, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 0.86)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.48387164783
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 1.53189579439
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 1.5318957943884857, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.53)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'left')
1.70541326551
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.99856493062
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.9985649306247268, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.00)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'forward', 'left')
2.1170491777
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.49896686808
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'left'), 'deadline': 13, 't': 12, 'action': None, 'reward': 1.4989668680841692, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.50)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'forward', 'left')
1.14715896175
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: forward, reward: 0.0271932247276
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 0.027193224727633858, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 0.03)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
1.3488885289
Environment.act() [POST]: location: (4, 7), heading: (-1, 0), action: None, reward: 2.6970503189
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 14, 'action': None, 'reward': 2.697050318896921, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.70)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'green', None, None)
1.48735728603
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: left, reward: 1.1247573951
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 10, 't': 15, 'action': 'left', 'reward': 1.1247573951018897, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.12)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'left', None)
1.97138835053
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.15633137094
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.1563313709391265, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.16)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', 'left', None)
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.48893847232
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 8, 't': 17, 'action': None, 'reward': 1.488938472324949, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.49)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', 'left', 'left')
1.7832664598
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: None, reward: 1.66422147101
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', 'left'), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.6642214710140915, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.66)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'green', 'left', None)
0.856922827256
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: right, reward: 0.805481161583
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 0.8054811615829222, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.81)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', None, 'left')
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: left, reward: -9.32222653068
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 5, 't': 20, 'action': 'left', 'reward': -9.32222653067762, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -9.32)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'red', None, 'left')
1.85198909807
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: None, reward: 0.981928140664
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 4, 't': 21, 'action': None, 'reward': 0.9819281406640479, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.98)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', None, None)
1.30605734056
Environment.act() [POST]: location: (3, 3), heading: (0, 1), action: left, reward: 0.954201207334
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 3, 't': 22, 'action': 'left', 'reward': 0.9542012073339428, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.95)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'green', None, None)
1.22655137086
Environment.act() [POST]: location: (2, 3), heading: (-1, 0), action: right, reward: 0.604957714472
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 0.6049577144724902, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.60)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('right', 'green', None, None)
1.24841726023
Environment.act() [POST]: location: (2, 2), heading: (0, -1), action: right, reward: 0.476296490443
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 0.47629649044317657, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.48)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 401
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (6, 6), deadline = 20
0.217882358883
Simulating trial. . .
epsilon = 0.2179; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2179; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2179; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2179; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2179; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2179; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', None, 'right')
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.77342332646
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.7734233264626296, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.77)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
2.20062890316
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 2.2371856703
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.237185670295715, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.24)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
2.21890728673
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.0305158356
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.0305158355996158, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.03)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.62471156116
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.06873017672
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.068730176723484, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.07)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'right', 'forward')
1.23415538206
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 2.55635619887
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.556356198866019, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 2.56)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'right', None)
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: right, reward: 2.83191945688
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.831919456883347, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 2.83)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
1.34672086894
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 2.07309030979
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.073090309788697, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.07)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
1.80171430122
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 1.92509881136
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.9250988113563232, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.93)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
1.86340655629
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 1.12362955064
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.1236295506396976, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.12)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
2.06566263029
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: forward, reward: 2.24621012875
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 2.2462101287487526, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.25)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'forward', None)
1.49351805346
Environment.act() [POST]: location: (6, 7), heading: (0, -1), action: None, reward: 1.23020109109
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.230201091089603, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.23)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
2.15593637952
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: forward, reward: 1.69827362826
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.6982736282615398, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.70)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 402
\-------------------------
Environment.reset(): Trial set up with start = (4, 4), destination = (8, 4), deadline = 20
0.217055977039
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2171; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', 'forward')
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.5136428409
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.513642840896709, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.51)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
2.07390050024
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.74944594715
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.7494459471455017, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.75)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
2.41167322369
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.60157228643
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.6015722864323916, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.60)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.50662275506
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.03166829333
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.0316682933289445, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.03)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
2.2691455242
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.7536861428
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.753686142801831, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.75)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.33941998787
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: forward, reward: 1.90970221653
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.9097022165255755, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.91)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -10.943771853
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': -10.943771853005327, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -10.94)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
2.5114158335
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: None, reward: 2.36678515995
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.3667851599548833, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.37)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (3, 4), heading: (-1, 0), action: left, reward: -9.80511799592
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': 'left', 'reward': -9.805117995918067, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving left through a red light. (rewarded -9.81)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: forward, reward: 2.69980005739
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 2.699800057391612, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.70)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, None)
1.50788372111
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 1.28147751549
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 1.281477515487658, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.28)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'left', 'right')
2.01034835532
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.34591839518
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.345918395176757, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 1.35)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', 'left', None)
2.43910049673
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.74925447246
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.7492544724598458, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.75)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'left', None)
2.16218057979
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 1.02366776929
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 7, 't': 13, 'action': 'forward', 'reward': 1.0236677692943401, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.02)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 403
\-------------------------
Environment.reset(): Trial set up with start = (8, 5), destination = (3, 2), deadline = 30
0.216232729488
Simulating trial. . .
epsilon = 0.2162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2162; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2162; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.93881696762
Environment.act() [POST]: location: (8, 5), heading: (0, -1), action: None, reward: 1.00678596413
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.006785964126742, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.01)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'right', None)
1.46599394757
Environment.act() [POST]: location: (1, 5), heading: (1, 0), action: right, reward: 2.28947474453
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 29, 't': 1, 'action': 'right', 'reward': 2.289474744534127, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 2.29)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: forward, reward: 1.97576846377
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': 1.975768463767247, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.98)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: left, reward: -10.1456524095
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 27, 't': 3, 'action': 'left', 'reward': -10.145652409456279, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -10.15)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
2.09417748459
Environment.act() [POST]: location: (2, 5), heading: (1, 0), action: None, reward: 2.73814897785
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.7381489778488204, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.74)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
1.59292417454
Environment.act() [POST]: location: (3, 5), heading: (1, 0), action: forward, reward: 1.17741676718
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 1.1774167671849622, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.18)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'left', None)
1.99633264076
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: right, reward: 1.0345157546
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 24, 't': 6, 'action': 'right', 'reward': 1.0345157545984973, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.03)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.70990558937
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 1.62565947924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.6256594792398908, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.63)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (3, 6), heading: (0, 1), action: None, reward: 2.22560351038
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.225603510379853, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.23)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, None)
1.68522454103
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: forward, reward: 2.3289756407
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 2.3289756406988413, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.33)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', None, 'right')
2.17301117243
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: forward, reward: 2.08470504594
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'right'), 'deadline': 20, 't': 10, 'action': 'forward', 'reward': 2.0847050459370524, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'right')
Agent followed the waypoint forward. (rewarded 2.08)
63% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 404
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (6, 4), deadline = 20
0.21541260434
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2154; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
1.47280146587
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.16842597672
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 0, 'action': None, 'reward': 2.1684259767227365, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.17)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
1.8206137213
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.74891648442
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.7489164844163947, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.75)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
1.78476510286
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.83792606818
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.8379260681842176, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.84)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
1.81134558552
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.93845180156
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.9384518015627297, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.94)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
2.37489869354
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.50337799641
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.5033779964148146, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.50)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', None, None)
2.43913834498
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.84293842081
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 5, 'action': None, 'reward': 2.842938420812102, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.84)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, None)
0.862356875337
Environment.act() [POST]: location: (8, 3), heading: (0, 1), action: right, reward: 1.86021165993
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 14, 't': 6, 'action': 'right', 'reward': 1.8602116599264686, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.86)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', 'left', None)
1.91271853144
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: right, reward: 2.81810180166
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 2.818101801658761, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.82)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', 'forward')
1.421843515
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.59785476385
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.5978547638450904, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 1.60)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
2.41616323122
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.82753279518
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.8275327951818494, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.83)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
1.38517047086
Environment.act() [POST]: location: (6, 3), heading: (-1, 0), action: forward, reward: 2.72165567821
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 10, 't': 10, 'action': 'forward', 'reward': 2.7216556782086743, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.72)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
1.13012927395
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 4), heading: (0, 1), action: left, reward: 2.30944032964
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 9, 't': 11, 'action': 'left', 'reward': 2.309440329636563, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.31)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 405
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (5, 5), deadline = 30
0.214595589755
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2146; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'forward', 'forward')
2.39463947142
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.67863201255
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.6786320125545489, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.68)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'forward', 'right')
1.63271480032
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.71829285034
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.718292850336396, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.72)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'right', None)
2.2417369862
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.8495038679
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.8495038678970515, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.85)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
2.1218480132
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.48538887601
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.485388876006786, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.49)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
1.8036184446
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.48325635112
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.4832563511226584, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.48)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
1.64343739786
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.13265047856
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 1.1326504785625127, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.13)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
2.05341307454
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 1.78768169272
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 1.7876816927185288, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.79)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', 'left')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 1.52850296287
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.5285029628661313, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.53)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', None)
1.36185957228
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: None, reward: 1.57551736869
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.5755173686894506, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.58)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'forward', None)
1.92710500389
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 1.5765127622
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 1.5765127621959274, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.58)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', 'right', None)
1.87773434605
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 2.60163108753
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 20, 't': 10, 'action': 'right', 'reward': 2.6016310875290074, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 2.60)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', 'right', None)
2.04562042705
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 1.80637807806
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 19, 't': 11, 'action': None, 'reward': 1.806378078061867, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.81)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
1.6677825343
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 1.6227256381
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.6227256381037753, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.62)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
2.00710009087
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: 2.23082394291
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 2.230823942908147, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.23)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
1.6452540862
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 2.09685206234
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 2.0968520623367897, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.10)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, None)
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: -9.38954502426
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': -9.389545024261228, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.39)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, 'right')
1.7068177784
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: None, reward: 1.31407761814
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 14, 't': 16, 'action': None, 'reward': 1.3140776181438094, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.31)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', None, None)
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: forward, reward: 2.64083600036
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 17, 'action': 'forward', 'reward': 2.640836000359347, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.64)
40% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 406
\-------------------------
Environment.reset(): Trial set up with start = (7, 6), destination = (5, 2), deadline = 20
0.213781673933
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2138; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, None)
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: forward, reward: -9.82526041551
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -9.825260415513899, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -9.83)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, 'right')
1.62811524269
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: None, reward: 1.94125199914
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.9412519991412502, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.94)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
2.0229694239
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: None, reward: 2.42670523743
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.4267052374286284, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.43)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
2.22483733066
Environment.act() [POST]: location: (7, 6), heading: (0, -1), action: None, reward: 2.82799480696
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.8279948069625513, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.83)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, None)
1.71978480179
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: left, reward: 1.5877800158
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 1.5877800158049906, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.59)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', None, 'forward')
2.14054711856
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.96171152645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 15, 't': 5, 'action': None, 'reward': 1.9617115264464362, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.96)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', None)
1.46868847048
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 1.25831225575
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.2583122557497355, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.26)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: right, reward: 0.928570763951
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 0.9285707639510756, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.93)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
2.52641606881
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 2.74239383255
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.742393832547714, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.74)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
2.63440495068
Environment.act() [POST]: location: (6, 5), heading: (0, -1), action: None, reward: 1.48669279925
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 9, 'action': None, 'reward': 1.486692799249417, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.49)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'green', 'right', None)
0.750735303418
Environment.act() [POST]: location: (7, 5), heading: (1, 0), action: right, reward: 0.543370387138
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 0.5433703871381396, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.54)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'right', None)
2.66183541571
Environment.act() [POST]: location: (7, 6), heading: (0, 1), action: right, reward: 0.997740087141
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.9977400871413471, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', None)
Agent followed the waypoint right. (rewarded 1.00)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: right, reward: 1.82668935375
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.8266893537454691, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.83)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', None, 'left')
1.72269948102
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 0.856938190779
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 7, 't': 13, 'action': None, 'reward': 0.8569381907787459, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.86)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, 'left')
1.2898188359
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 0.721788717966
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.7217887179656415, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.72)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'red', None, 'right')
1.51044769827
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 0.609040608764
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 5, 't': 15, 'action': None, 'reward': 0.6090406087635611, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 0.61)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', 'forward', None)
1.36350036312
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: None, reward: 0.838342487323
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': None, 'reward': 0.8383424873226979, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.84)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'green', 'forward', None)
1.75180888304
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.94766706452
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 3, 't': 17, 'action': 'forward', 'reward': 1.9476670645158363, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.95)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, None)
2.06054887497
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 0.800613080369
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.800613080369406, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.80)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, 'right')
1.78468362092
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.643744666
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.6437446659972745, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.64)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 407
\-------------------------
Environment.reset(): Trial set up with start = (4, 7), destination = (7, 3), deadline = 25
0.212970845123
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2130; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
1.6537824088
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: left, reward: 2.81028246372
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 25, 't': 0, 'action': 'left', 'reward': 2.810282463721502, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.81)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
2.37989900862
Environment.act() [POST]: location: (6, 7), heading: (1, 0), action: forward, reward: 2.76076790272
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 2.760767902715264, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.76)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'left')
1.68179867935
Environment.act() [POST]: location: (7, 7), heading: (1, 0), action: forward, reward: 1.41977345676
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 23, 't': 2, 'action': 'forward', 'reward': 1.4197734567633458, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.42)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'left', None)
2.36541016655
Environment.act() [POST]: location: (7, 2), heading: (0, 1), action: right, reward: 2.42439126672
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': 'right', 'reward': 2.4243912667248173, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 2.42)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.92054738363
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 3), heading: (0, 1), action: forward, reward: 2.94282270598
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 2.9428227059758783, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.94)
80% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 408
\-------------------------
Environment.reset(): Trial set up with start = (1, 4), destination = (6, 5), deadline = 20
0.212163091615
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2122; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, 'left')
Environment.act() [POST]: location: (1, 5), heading: (0, 1), action: right, reward: 2.03358624143
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.033586241431928, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 2.03)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'left', None)
2.39490071664
Environment.act() [POST]: location: (8, 5), heading: (-1, 0), action: right, reward: 1.66365251458
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.6636525145777306, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 1.66)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: 2.62346406084
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 2.6234640608374233, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.62)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.38804393821
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: None, reward: 1.90853917316
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.908539173160139, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.91)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (7, 5), heading: (-1, 0), action: forward, reward: -39.884103561
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -39.88410356103414, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.88)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
2.4316850448
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: forward, reward: 2.07885615475
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.0788561547467426, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.08)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 409
\-------------------------
Environment.reset(): Trial set up with start = (8, 6), destination = (5, 3), deadline = 30
0.211358401746
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2114; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', None, None)
2.6410383829
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: None, reward: 2.52390204675
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 30, 't': 0, 'action': None, 'reward': 2.5239020467477973, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.52)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
2.58247021482
Environment.act() [POST]: location: (8, 6), heading: (0, 1), action: None, reward: 1.84045710686
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.8404571068614632, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.84)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, 'left')
1.89905745303
Environment.act() [POST]: location: (7, 6), heading: (-1, 0), action: right, reward: 1.50078721574
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 28, 't': 2, 'action': 'right', 'reward': 1.5007872157384123, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.50)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'forward')
2.21398398115
Environment.act() [POST]: location: (6, 6), heading: (-1, 0), action: forward, reward: 0.989499651001
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 27, 't': 3, 'action': 'forward', 'reward': 0.9894996510012448, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.99)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
1.55078606805
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: forward, reward: 1.46654679119
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 26, 't': 4, 'action': 'forward', 'reward': 1.4665467911875514, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.47)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', 'left')
0.587176093237
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 0.912192785045
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'left'), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 0.9121927850453629, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'left')
Agent drove forward instead of left. (rewarded 0.91)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'forward', 'forward')
1.02946736489
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: 0.747800333659
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', 'forward'), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 0.7478003336591784, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', 'forward')
Agent drove forward instead of left. (rewarded 0.75)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
2.2373173674
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: 1.93227717062
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 23, 't': 7, 'action': 'left', 'reward': 1.9322771706236173, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 1.93)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: left, reward: 2.44520696071
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 22, 't': 8, 'action': 'left', 'reward': 2.4452069607133065, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.45)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, None)
1.87105307427
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.1900987852
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 9, 'action': None, 'reward': 1.1900987852048097, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.19)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: right, reward: -20.3834562607
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 20, 't': 10, 'action': 'right', 'reward': -20.383456260688032, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.38)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'forward', None)
1.84973797378
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: 1.33723347603
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 11, 'action': 'forward', 'reward': 1.337233476030565, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.34)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'left', None)
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: None, reward: 1.57642452655
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.57642452655234, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.58)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'left', None)
1.51542419768
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: right, reward: 1.56948465324
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 1.5694846532413445, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.57)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', None, None)
1.53057592974
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: None, reward: 1.58729991198
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.5872999119805618, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.59)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', None, None)
2.57033345567
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 3), heading: (0, 1), action: forward, reward: 1.34112157217
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 15, 'action': 'forward', 'reward': 1.3411215721703453, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.34)
47% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 410
\-------------------------
Environment.reset(): Trial set up with start = (2, 4), destination = (7, 3), deadline = 20
0.210556763896
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2106; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (2, 4), heading: (-1, 0), action: None, reward: 0.319974530186
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': None, 'reward': 0.31997453018622624, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent idled at a green light with oncoming traffic. (rewarded 0.32)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', 'left')
1.75835395893
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: forward, reward: 2.18699119541
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 2.1869911954088197, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.19)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', None)
1.10092142522
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 2.71308031059
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.7130803105909203, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.71)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
1.90700086791
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.66417795648
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.6641779564789296, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.66)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
1.78558941219
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: None, reward: 1.45855087017
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.4585508701683543, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.46)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', 'right')
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: -20.5590394319
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'forward', 'right'), 'deadline': 15, 't': 5, 'action': 'left', 'reward': -20.559039431921924, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'right')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.56)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'left')
1.50866642962
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 1.1222485256
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.1222485255980923, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.12)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
1.55893792086
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.68309036774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.683090367739881, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.68)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', 'left')
2.12794929932
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.13232689625
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.1323268962495998, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.13)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
1.64829155569
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.65198453287
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.6519845328674583, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.65)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
2.15013804428
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 1.77614824283
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.7761482428330795, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.78)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: right, reward: -0.261136659068
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': -0.2611366590684441, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove right instead of forward. (rewarded -0.26)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
1.43058097767
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 1.79666736229
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.7966673622914096, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.80)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 1.07013298869
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.0701329886850437, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 1.07)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
2.21146366084
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 1.13938629892
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 1.1393862989186188, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.14)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', None, None)
1.67542497988
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: None, reward: 2.13288673615
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 5, 't': 15, 'action': None, 'reward': 2.132886736152501, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.13)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'green', None, 'left')
2.4428358008
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: right, reward: 0.617440578425
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 0.6174405784249328, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 0.62)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'left', None)
1.54245442546
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 1.87957822449
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 1.879578224494854, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 1.88)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', None)
1.62207014118
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 0.649231984864
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 2, 't': 18, 'action': None, 'reward': 0.6492319848640813, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.65)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'forward', None)
1.13565106302
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 0.593243112756
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 0.5932431127555167, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.59)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 411
\-------------------------
Environment.reset(): Trial set up with start = (8, 7), destination = (5, 4), deadline = 30
0.209758166489
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2098; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'right')
1.47627551076
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: right, reward: 1.07275145414
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'right'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 1.0727514541366194, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'right')
Agent followed the waypoint right. (rewarded 1.07)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'left', 'forward')
1.90856768298
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 2.69697925763
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'forward'), 'deadline': 29, 't': 1, 'action': 'forward', 'reward': 2.696979257628491, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'forward')
Agent followed the waypoint forward. (rewarded 2.70)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
1.95572751392
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: forward, reward: 2.06278924491
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 28, 't': 2, 'action': 'forward', 'reward': 2.0627892449128633, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.06)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, 'left')
1.41695861937
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.0522685962
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.052268596198585, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.05)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'right', 'left')
1.01081833127
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: right, reward: 0.67283302779
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', 'left'), 'deadline': 26, 't': 4, 'action': 'right', 'reward': 0.6728330277904749, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', 'left')
Agent drove right instead of left. (rewarded 0.67)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: forward, reward: 2.50248257689
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 2.5024825768920356, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.50)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'forward')
2.0511293225
Environment.act() [POST]: location: (5, 5), heading: (0, -1), action: None, reward: 2.5741568069
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.574156806901619, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.57)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, 'forward')
1.60174181607
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 4), heading: (0, -1), action: forward, reward: 2.79506701857
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 2.7950670185714035, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.80)
73% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 412
\-------------------------
Environment.reset(): Trial set up with start = (1, 2), destination = (5, 6), deadline = 30
0.208962597994
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2090; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'right', 'forward')
1.70730265856
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: right, reward: 2.36052457623
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 30, 't': 0, 'action': 'right', 'reward': 2.360524576233086, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent followed the waypoint right. (rewarded 2.36)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
1.96314314356
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.62169859247
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 29, 't': 1, 'action': None, 'reward': 2.6216985924731064, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.62)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'forward')
1.50984913942
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.57384028337
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'forward'), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.5738402833678227, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'forward')
Agent properly idled at a red light. (rewarded 2.57)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', 'left')
2.0837266825
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.68053000892
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.6805300089191277, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 1.68)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
2.29242086801
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 1.73651463948
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.7365146394780386, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.74)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
2.01446775375
Environment.act() [POST]: location: (8, 2), heading: (-1, 0), action: None, reward: 2.67030004461
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 25, 't': 5, 'action': None, 'reward': 2.6703000446087213, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.67)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', 'left')
1.97267257717
Environment.act() [POST]: location: (7, 2), heading: (-1, 0), action: forward, reward: 2.7488659671
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 2.748865967096943, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 2.75)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
2.00925837942
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: forward, reward: 1.36728567257
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 1.3672856725708324, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.37)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
Environment.act() [POST]: location: (6, 2), heading: (-1, 0), action: left, reward: -10.5962200465
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 22, 't': 8, 'action': 'left', 'reward': -10.596220046478077, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent attempted driving left through a red light. (rewarded -10.60)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', None, 'left')
1.31545747761
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: forward, reward: 2.58437530544
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 21, 't': 9, 'action': 'forward', 'reward': 2.584375305442236, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.58)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
1.90415585802
Environment.act() [POST]: location: (5, 2), heading: (-1, 0), action: None, reward: 2.7989124451
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.7989124451005063, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.80)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', None, None)
1.59398681069
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: right, reward: 1.69384916526
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 19, 't': 11, 'action': 'right', 'reward': 1.6938491652560743, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.69)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
1.6210141443
Environment.act() [POST]: location: (5, 7), heading: (0, -1), action: None, reward: 1.94026061229
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.9402606122860016, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.94)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.68827202599
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 6), heading: (0, -1), action: forward, reward: 1.71566036321
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 1.7156603632134246, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.72)
53% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 413
\-------------------------
Environment.reset(): Trial set up with start = (8, 4), destination = (2, 2), deadline = 20
0.208170046922
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
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epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2082; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'left', None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: left, reward: 0.200805358095
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 0.20080535809474798, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent drove left instead of forward. (rewarded 0.20)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, None)
2.35153415156
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 1.00601344246
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.006013442455984, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.01)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: forward, reward: -10.8743619246
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': -10.874361924603596, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.87)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', None, None)
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 2.5307723907
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.5307723906955495, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.53)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', None, None)
2.10477309385
Environment.act() [POST]: location: (8, 3), heading: (0, -1), action: None, reward: 1.43440818094
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.434408180944814, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.43)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, None)
1.64391798797
Environment.act() [POST]: location: (1, 3), heading: (1, 0), action: right, reward: 1.95470579242
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.954705792419498, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.95)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, 'forward')
2.19840441732
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: 2.51859795712
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.5185979571159827, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 2.52)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
1.61362416998
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.00822180511
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.008221805111986, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.01)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, None)
1.81092298755
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 2.10062522056
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.10062522056409, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.10)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: forward, reward: -39.5066950113
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': -39.50669501129554, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.51)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', 'forward')
1.94978228953
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: None, reward: 1.58360033691
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.5836003369094596, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.58)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'right', None)
0.647052845278
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 0.956019123177
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 9, 't': 11, 'action': 'right', 'reward': 0.9560191231774879, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.96)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', 'forward', None)
1.24826481123
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 1.2544686329
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 8, 't': 12, 'action': None, 'reward': 1.254468632902583, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.25)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'forward', 'forward')
1.96879992232
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: right, reward: 1.52612520968
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 7, 't': 13, 'action': 'right', 'reward': 1.526125209676092, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 1.53)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', None, 'right')
2.29068668151
Environment.act() [POST]: location: (1, 3), heading: (0, -1), action: right, reward: 0.565263754947
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.5652637549465676, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 0.57)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'green', None, None)
1.7993118902
Environment.act() [POST]: location: (2, 3), heading: (1, 0), action: right, reward: 1.72715574518
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 5, 't': 15, 'action': 'right', 'reward': 1.7271557451755297, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.73)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: 1.37562097451
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.3756209745050418, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded 1.38)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, 'forward')
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: right, reward: -20.5668991623
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 3, 't': 17, 'action': 'right', 'reward': -20.566899162271962, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.57)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'forward', 'right')
1.93774765896
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 1.93542037028
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 2, 't': 18, 'action': None, 'reward': 1.9354203702829311, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.94)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'forward', None)
1.25136672207
Environment.act() [POST]: location: (2, 4), heading: (0, 1), action: None, reward: 1.17577643535
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.1757764353501532, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.18)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 414
\-------------------------
Environment.reset(): Trial set up with start = (4, 2), destination = (2, 5), deadline = 25
0.20738050183
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2074; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'left')
1.78845945448
Environment.act() [POST]: location: (3, 2), heading: (-1, 0), action: forward, reward: 1.07802722652
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 1.078027226521229, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.08)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'right', 'forward')
1.89525579046
Environment.act() [POST]: location: (2, 2), heading: (-1, 0), action: forward, reward: 1.41043702478
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 1.4104370247825373, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 1.41)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'green', None, None)
1.76323381769
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: right, reward: 2.75407199054
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 2.754071990540924, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.75)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.78063737829
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 2.53769569086
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.5376956908606783, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.54)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: right, reward: 1.07252515446
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.0725251544617795, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent drove right instead of forward. (rewarded 1.07)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', None, None)
1.95577410405
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 2.73376960826
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.7337696082571927, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.73)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'forward')
1.63910506811
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 1.10791537581
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 19, 't': 6, 'action': None, 'reward': 1.107915375809665, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.11)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 0.60070470609
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 0.6007047060898727, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove forward instead of left. (rewarded 0.60)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
2.33861969849
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: left, reward: 1.29831324971
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 1.2983132497117484, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.30)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, None)
2.34477185616
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 2.76157693711
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 2.7615769371093255, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.76)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'right', None)
2.17410621163
Environment.act() [POST]: location: (4, 6), heading: (0, -1), action: None, reward: 2.13357209441
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.1335720944149035, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.13)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', None, None)
1.8184664741
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: left, reward: 1.67782185384
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 1.677821853840419, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.68)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.7019661946
Environment.act() [POST]: location: (2, 6), heading: (-1, 0), action: forward, reward: 1.69367375946
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'forward', 'reward': 1.6936737594628648, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.69)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', None, None)
2.25865290411
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: right, reward: 2.2778676746
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 2.27786767460125, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 2.28)
44% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 415
\-------------------------
Environment.reset(): Trial set up with start = (8, 6), destination = (3, 7), deadline = 20
0.206593951315
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2066; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'left')
1.50503193837
Environment.act() [POST]: location: (1, 6), heading: (1, 0), action: right, reward: 2.65611250642
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.6561125064217936, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.66)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'forward')
2.35850118722
Environment.act() [POST]: location: (2, 6), heading: (1, 0), action: forward, reward: 1.9014189553
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 1.9014189552957284, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 1.90)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'forward', 'left')
1.4332433405
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: forward, reward: 1.62964704465
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 18, 't': 2, 'action': 'forward', 'reward': 1.6296470446546865, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.63)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', 'right')
1.93658401462
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: None, reward: 1.62063929929
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.6206392992850798, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.62)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', None)
1.21357157871
Environment.act() [POST]: location: (3, 6), heading: (1, 0), action: None, reward: 2.90639130094
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.9063913009443647, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.91)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'forward', None)
1.72751536225
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: right, reward: 2.03808709995
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.038087099952198, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.04)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 416
\-------------------------
Environment.reset(): Trial set up with start = (3, 3), destination = (6, 4), deadline = 20
0.205810384021
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2058; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', None, 'left')
1.94991639153
Environment.act() [POST]: location: (4, 3), heading: (1, 0), action: forward, reward: 1.47958291158
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.4795829115849415, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.48)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'right', 'forward')
1.65284640762
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: forward, reward: 1.85336678929
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 1.8533667892898211, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 1.85)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'right', None)
1.92599925256
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 2.58102137797
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.5810213779702815, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.58)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
2.15916653458
Environment.act() [POST]: location: (5, 3), heading: (1, 0), action: None, reward: 1.77923107937
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.779231079367564, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.78)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, 'left')
1.25532000654
Environment.act() [POST]: location: (5, 4), heading: (0, 1), action: right, reward: 1.47566989924
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 1.4756698992404598, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded 1.48)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
1.74814416397
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 4), heading: (1, 0), action: left, reward: 1.7687701849
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.7687701848961004, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.77)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 417
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (7, 6), deadline = 25
0.205029788632
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2050; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', None)
1.2622164884
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: forward, reward: 2.78806187888
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 2.788061878883532, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 2.79)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
1.96919880697
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.85491391763
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.8549139176254146, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.85)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'left')
1.36549495289
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 0.441338407044
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 23, 't': 2, 'action': 'right', 'reward': 0.44133840704357374, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent drove right instead of forward. (rewarded 0.44)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'green', None, 'left')
1.10053842382
Environment.act() [POST]: location: (1, 4), heading: (-1, 0), action: left, reward: 1.63711543501
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 22, 't': 3, 'action': 'left', 'reward': 1.637115435007619, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.64)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
1.71474965156
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: forward, reward: 1.14787123701
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 21, 't': 4, 'action': 'forward', 'reward': 1.1478712370106605, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 1.15)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'right', 'forward')
1.61155791939
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: None, reward: 2.72211739736
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'forward'), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.722117397361817, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 2.72)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'right', 'right')
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: left, reward: -20.6436932611
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 3, 'light': 'green', 'state': ('forward', 'green', 'right', 'right'), 'deadline': 19, 't': 6, 'action': 'left', 'reward': -20.643693261073896, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'right')
Agent attempted driving left through traffic and cause a minor accident. (rewarded -20.64)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
1.69781997703
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 2.33181722
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 7, 'action': 'forward', 'reward': 2.3318172199957683, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.33)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'green', None, None)
1.75845717443
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: left, reward: 2.17137359082
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 17, 't': 8, 'action': 'left', 'reward': 2.171373590818526, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.17)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.29997835005
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.2999783500541975, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.30)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
2.4120563623
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.22794279864
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.2279427986400284, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.23)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, 'forward')
1.80631070738
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.21489083531
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.2148908353132093, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.21)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, 'right')
1.27275452811
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 0.214251147644
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'right'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 0.21425114764406517, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'right')
Agent drove right instead of forward. (rewarded 0.21)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'red', None, None)
2.55317439663
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 1.96042948003
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 12, 't': 13, 'action': None, 'reward': 1.9604294800269662, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.96)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', None, None)
2.25680193833
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 0.946713732645
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 14, 'action': None, 'reward': 0.9467137326448887, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.95)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', None, 'right')
1.71421414346
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: None, reward: 2.07168470421
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 10, 't': 15, 'action': None, 'reward': 2.0716847042072493, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 2.07)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'right', None)
0.801535984228
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: right, reward: 1.42575145371
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 9, 't': 16, 'action': 'right', 'reward': 1.4257514537082887, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 1.43)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'red', None, None)
1.7695906374
Environment.act() [POST]: location: (6, 4), heading: (0, -1), action: None, reward: 1.81613747974
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 17, 'action': None, 'reward': 1.8161374797384213, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.82)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', 'left', 'left')
2.0805722224
Environment.act() [POST]: location: (7, 4), heading: (1, 0), action: right, reward: 1.34484401703
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 7, 't': 18, 'action': 'right', 'reward': 1.3448440170250238, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.34)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
2.26826028936
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: right, reward: 1.77171298183
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 1.7717129818260506, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.77)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', None, 'forward')
1.51060077135
Environment.act() [POST]: location: (7, 5), heading: (0, 1), action: None, reward: 1.50777518899
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 5, 't': 20, 'action': None, 'reward': 1.507775188994661, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.51)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', None, 'forward')
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: -0.65601982278
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 4, 't': 21, 'action': 'right', 'reward': -0.6560198227798911, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent drove right instead of forward. (rewarded -0.66)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('left', 'green', 'forward', None)
1.14019745765
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: -0.294649140078
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': -0.2946491400784692, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove forward instead of left. (rewarded -0.29)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('left', 'red', 'forward', 'right')
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: left, reward: -9.08140720463
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'forward', 'right'), 'deadline': 2, 't': 23, 'action': 'left', 'reward': -9.081407204625169, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'right')
Agent attempted driving left through a red light. (rewarded -9.08)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'red', 'forward', None)
1.46509856524
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: None, reward: 1.53826655177
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 1, 't': 24, 'action': None, 'reward': 1.5382665517686391, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.54)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 418
\-------------------------
Environment.reset(): Trial set up with start = (2, 7), destination = (6, 3), deadline = 30
0.204252153877
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2043; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', 'forward')
1.75310659846
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: forward, reward: 1.78104753901
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 30, 't': 0, 'action': 'forward', 'reward': 1.7810475390069596, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 1.78)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'forward')
1.50918798017
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.81213230079
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.812132300791464, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.81)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.81999958047
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 1.2495197759
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 28, 't': 2, 'action': None, 'reward': 1.2495197759001158, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.25)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.53475967818
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.22146068576
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 27, 't': 3, 'action': None, 'reward': 2.2214606857595722, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.22)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
1.87811018197
Environment.act() [POST]: location: (1, 7), heading: (-1, 0), action: None, reward: 2.06430112744
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 26, 't': 4, 'action': None, 'reward': 2.0643011274446432, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.06)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
1.59348572491
Environment.act() [POST]: location: (8, 7), heading: (-1, 0), action: forward, reward: 2.33673719141
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 25, 't': 5, 'action': 'forward', 'reward': 2.336737191406021, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.34)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', None, None)
2.01481859851
Environment.act() [POST]: location: (7, 7), heading: (-1, 0), action: forward, reward: 1.36283664326
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 6, 'action': 'forward', 'reward': 1.3628366432645116, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.36)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, 'forward')
2.12996007126
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: forward, reward: 0.944337482226
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'forward'), 'deadline': 23, 't': 7, 'action': 'forward', 'reward': 0.9443374822256172, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'forward')
Agent followed the waypoint forward. (rewarded 0.94)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'right', None)
2.15383915302
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: None, reward: 1.2811318933
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 22, 't': 8, 'action': None, 'reward': 1.2811318932982583, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.28)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
1.96491538263
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: left, reward: 2.42884931656
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 21, 't': 9, 'action': 'left', 'reward': 2.428849316564814, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.43)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'right', None)
2.25351031526
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 2.43316039816
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.4331603981569723, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 2.43)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
1.97120565471
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 2.1741823002
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 11, 'action': None, 'reward': 2.1741823001969087, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.17)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
2.07269397745
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: None, reward: 1.45737158655
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.457371586545457, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.46)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
1.68882762089
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 3), heading: (0, 1), action: forward, reward: 1.53665254159
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 13, 'action': 'forward', 'reward': 1.536652541593416, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.54)
53% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 419
\-------------------------
Environment.reset(): Trial set up with start = (8, 3), destination = (5, 7), deadline = 25
0.203477468527
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
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epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2035; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: right, reward: 1.64596226505
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.6459622650476156, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent drove right instead of forward. (rewarded 1.65)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'left', None)
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: forward, reward: -9.49634562049
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -9.496345620494427, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.50)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.52639916653
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.065769658
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.0657696579970735, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.07)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
1.29608441226
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 2.45614575563
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 2.456145755626479, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.46)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
1.87611508395
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 2.37749861969
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 2.3774986196949195, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.38)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'red', 'left', None)
2.12680685182
Environment.act() [POST]: location: (8, 2), heading: (0, -1), action: None, reward: 1.89649024769
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 1.8964902476879923, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.90)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'green', 'left', None)
0.831201994419
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: right, reward: 1.78354025629
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.783540256290217, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.78)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'left', None)
2.01164854975
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 2.32181305672
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.321813056717369, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.32)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'left', None)
2.16673080324
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 1.28165091892
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.2816509189205703, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.28)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', 'left', 'forward')
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: -39.4425551459
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('left', 'red', 'left', 'forward'), 'deadline': 16, 't': 9, 'action': 'forward', 'reward': -39.44255514592738, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -39.44)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'left', None)
1.72419086108
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: 0.968339850451
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 15, 't': 10, 'action': None, 'reward': 0.9683398504507021, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.97)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'left', 'left')
1.92178590936
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: 1.94146457009
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', 'left'), 'deadline': 14, 't': 11, 'action': 'left', 'reward': 1.9414645700935036, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', 'left')
Agent followed the waypoint left. (rewarded 1.94)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', 'left', None)
1.34626535576
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 0.794815875462
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 13, 't': 12, 'action': None, 'reward': 0.7948158754623704, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 0.79)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', 'left', None)
1.30737112535
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 0.982170426399
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 12, 't': 13, 'action': 'right', 'reward': 0.9821704263994782, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.98)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'right', None)
2.02513918364
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 2.04084663286
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': 2.0408466328574946, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 2.04)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: -4.8365743517
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 10, 't': 15, 'action': None, 'reward': -4.836574351697218, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.84)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('forward', 'red', None, None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: -10.8262156514
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 9, 't': 16, 'action': 'forward', 'reward': -10.826215651444482, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent attempted driving forward through a red light. (rewarded -10.83)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('forward', 'red', None, None)
1.765032782
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 1.15002652341
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 8, 't': 17, 'action': None, 'reward': 1.1500265234077092, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.15)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', None, 'forward')
1.66066014048
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 1.1344413068
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 7, 't': 18, 'action': None, 'reward': 1.1344413068014019, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.13)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'green', 'left', None)
2.37887658833
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 2.37068612261
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 6, 't': 19, 'action': 'forward', 'reward': 2.3706861226123053, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.37)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'green', None, None)
1.61274008124
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 7), heading: (1, 0), action: forward, reward: 1.89443157872
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 5, 't': 20, 'action': 'forward', 'reward': 1.8944315787159112, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.89)
16% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 420
\-------------------------
Environment.reset(): Trial set up with start = (5, 6), destination = (3, 2), deadline = 20
0.202705721394
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
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epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2027; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
2.1968823496
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: left, reward: 2.1885724937
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 2.1885724937001942, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.19)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.75358582998
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: 1.8661897478
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 1.866189747796765, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.87)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'left', None)
1.07054061561
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: None, reward: 2.62616992905
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.6261699290506533, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.63)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'left', None)
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: -10.8185080638
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': -10.818508063813429, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -10.82)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'left', None)
1.84835527233
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: None, reward: 2.2477054961
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.247705496098166, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.25)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'left', None)
1.14477077588
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: 1.57571962026
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.5757196202632235, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.58)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: left, reward: 0.871106019882
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'forward'), 'deadline': 14, 't': 6, 'action': 'left', 'reward': 0.8711060198817511, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'forward')
Agent drove left instead of right. (rewarded 0.87)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'forward')
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 1.63399805735
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.6339980573485864, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent drove right instead of left. (rewarded 1.63)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'forward', None)
2.05998143983
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: None, reward: 2.71181385707
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.7118138570722143, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.71)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', 'forward', None)
1.8828012311
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: right, reward: 2.31607757206
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 2.316077572059895, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.32)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', None, None)
1.60175783549
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 2.22683079738
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 10, 't': 10, 'action': None, 'reward': 2.22683079738313, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.23)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'red', None, 'left')
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: forward, reward: -10.2036421177
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': -10.203642117664542, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent attempted driving forward through a red light. (rewarded -10.20)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'red', None, None)
1.91429431644
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 2.1246697822
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 8, 't': 12, 'action': None, 'reward': 2.124669782195224, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.12)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, 'forward')
2.08479726901
Environment.act() [POST]: location: (3, 3), heading: (0, -1), action: left, reward: 2.32722453877
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 2.3272245387708206, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.33)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'green', 'forward', None)
1.96511145816
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (3, 2), heading: (0, -1), action: forward, reward: 0.898633056476
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 6, 't': 14, 'action': 'forward', 'reward': 0.8986330564762293, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 0.90)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 421
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (2, 5), deadline = 25
0.201936901336
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2019; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', 'left', None)
1.36024519807
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 1.5140664841
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 1.514066484098944, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 1.51)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.80988778889
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 2.58981971422
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': 2.5898197142218558, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.59)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.4575296527
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.52653104112
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.5265310411225612, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.53)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
2.19985375155
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: 2.13821375745
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 22, 't': 3, 'action': 'forward', 'reward': 2.1382137574484332, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.14)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (3, 7), heading: (0, 1), action: left, reward: 1.73947207993
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 21, 't': 4, 'action': 'left', 'reward': 1.7394720799327446, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 1.74)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', None, 'left')
1.53013818961
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: right, reward: 1.95021481724
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 20, 't': 5, 'action': 'right', 'reward': 1.9502148172435323, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.95)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, 'left')
1.8806953217
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.64375085386
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 19, 't': 6, 'action': None, 'reward': 2.6437508538585064, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.64)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
1.79286405857
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 2.31260769752
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.312607697518742, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.31)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', 'left', None)
Environment.act() [POST]: location: (2, 7), heading: (-1, 0), action: None, reward: 1.45545245301
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.4554524530112012, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.46)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', 'left', None)
2.02927661561
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: right, reward: 0.924454736631
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', None), 'deadline': 16, 't': 9, 'action': 'right', 'reward': 0.9244547366309681, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', None)
Agent followed the waypoint right. (rewarded 0.92)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'forward', 'left')
1.53144519258
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: 2.65938092799
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.659380927991638, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 2.66)
56% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 422
\-------------------------
Environment.reset(): Trial set up with start = (3, 4), destination = (5, 2), deadline = 20
0.201170997251
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2012; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'right', 'forward')
1.76707706873
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: forward, reward: 1.18604591772
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.186045917719659, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 1.19)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
1.49203034691
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 1.90444135046
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.9044413504569138, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.90)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
1.69823584868
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 2.18350546855
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.1835054685522026, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.18)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.94087065862
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 2.61261115447
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.6126111544717574, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.61)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', None, None)
2.27674090655
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: None, reward: 1.85084745848
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.8508474584795669, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.85)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', None, None)
2.1690337545
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: 2.53699230151
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.536992301513122, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.54)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('left', 'red', None, 'left')
1.23461360778
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: None, reward: 1.99572487949
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.9957248794918931, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.00)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'green', None, 'right')
1.49967506312
Environment.act() [POST]: location: (5, 3), heading: (0, -1), action: left, reward: 2.5645877243
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'right'), 'deadline': 13, 't': 7, 'action': 'left', 'reward': 2.564587724297705, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'right')
Agent followed the waypoint left. (rewarded 2.56)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'green', None, None)
2.35301302801
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 2), heading: (0, -1), action: forward, reward: 2.16947602782
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 8, 'action': 'forward', 'reward': 2.1694760278231486, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.17)
55% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 423
\-------------------------
Environment.reset(): Trial set up with start = (6, 5), destination = (5, 2), deadline = 20
0.200407998078
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
Simulating trial. . .
epsilon = 0.2004; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'green', 'forward', 'left')
Environment.act() [POST]: location: (5, 5), heading: (-1, 0), action: forward, reward: 1.16909962146
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': 1.1690996214552074, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 1.17)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'left')
1.36882692941
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: 2.65136191048
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 2.6513619104783492, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 2.65)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: left, reward: -10.3387131652
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 18, 't': 2, 'action': 'left', 'reward': -10.338713165210663, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving left through a red light. (rewarded -10.34)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'forward', None)
0.864447087889
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.76161849491
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.7616184949099951, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.76)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'forward', None)
1.3130327914
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.1976856954
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.1976856954035693, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.20)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', 'left')
1.63225634087
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: forward, reward: 2.50496153618
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.5049615361768685, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 2.50)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, None)
2.06379418251
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: None, reward: 2.69040464497
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 2.6904046449714687, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.69)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', None, None)
2.26124452792
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: forward, reward: 0.990070158621
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 7, 'action': 'forward', 'reward': 0.990070158620912, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.99)
60% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 424
\-------------------------
Environment.reset(): Trial set up with start = (7, 3), destination = (3, 4), deadline = 25
0.1996478928
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1996; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', 'right', None)
1.71748552316
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.67553964797
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 25, 't': 0, 'action': None, 'reward': 1.6755396479721782, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.68)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', 'forward', None)
1.5016825585
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.06440360146
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': None, 'reward': 1.0644036014577922, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.06)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', 'forward', None)
1.28304307998
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.28329821327
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.283298213271573, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.28)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', 'right', None)
1.69651258557
Environment.act() [POST]: location: (7, 3), heading: (-1, 0), action: None, reward: 1.88167351457
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'right', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.8816735145700314, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'right', None)
Agent properly idled at a red light. (rewarded 1.88)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', 'forward', None)
0.488865039096
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: right, reward: 1.03655871281
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 1.0365587128081253, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.04)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'red', 'forward', None)
2.38589764845
Environment.act() [POST]: location: (7, 2), heading: (0, -1), action: None, reward: 2.11458388458
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 20, 't': 5, 'action': None, 'reward': 2.1145838845772, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.11)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'green', 'forward', 'right')
2.23374158781
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: right, reward: 1.74673994786
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'right'), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.7467399478638348, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'right')
Agent followed the waypoint right. (rewarded 1.75)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, None)
2.37709941374
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.41773124142
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 7, 'action': None, 'reward': 1.4177312414199237, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.42)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', 'right')
1.22781794849
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 1.79584842066
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'right'), 'deadline': 17, 't': 8, 'action': None, 'reward': 1.7958484206619774, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'right')
Agent properly idled at a red light. (rewarded 1.80)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', None, 'forward')
1.39755072364
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 0.971519308918
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 16, 't': 9, 'action': None, 'reward': 0.9715193089176346, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.97)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
1.89741532758
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: None, reward: 2.82604744295
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 2.8260474429524645, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.83)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', None, None)
1.62565734327
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: 1.24882200012
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 14, 't': 11, 'action': 'forward', 'reward': 1.2488220001227917, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.25)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', None, None)
1.46122844499
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: left, reward: 0.723654357631
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 13, 't': 12, 'action': 'left', 'reward': 0.7236543576309273, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.72)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', 'forward', 'left')
1.80980523991
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 1.66174781377
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 12, 't': 13, 'action': None, 'reward': 1.6617478137714505, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.66)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', 'forward', 'right')
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: forward, reward: -9.81974884601
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 11, 't': 14, 'action': 'forward', 'reward': -9.819748846009611, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent attempted driving forward through a red light. (rewarded -9.82)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', None)
2.25024076651
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 1.63160153835
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.6316015383482416, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.63)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', 'left')
1.73577652684
Environment.act() [POST]: location: (1, 7), heading: (0, -1), action: None, reward: 1.54824459682
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 9, 't': 16, 'action': None, 'reward': 1.5482445968239655, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.55)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('right', 'green', 'forward', None)
2.09943940158
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: 2.2728044073
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 2.272804407298538, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 2.27)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: None, reward: 0.923545921594
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 7, 't': 18, 'action': None, 'reward': 0.9235459215937454, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 0.92)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: -20.6222812473
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 6, 't': 19, 'action': 'right', 'reward': -20.622281247275147, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -20.62)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('forward', 'red', 'forward', 'forward')
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: right, reward: -19.1763969086
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 3, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': -19.176396908598353, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent attempted driving right through traffic and cause a minor accident. (rewarded -19.18)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'green', 'forward', None)
1.43187225732
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 1.1104939191
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 4, 't': 21, 'action': 'forward', 'reward': 1.1104939191009808, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.11)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'green', 'left', 'left')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: -0.352724186568
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 3, 't': 22, 'action': None, 'reward': -0.3527241865678099, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent idled at a green light with oncoming traffic. (rewarded -0.35)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'green', 'left', 'left')
2.44442328209
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: right, reward: 1.54172224469
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 2, 't': 23, 'action': 'right', 'reward': 1.541722244690574, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 1.54)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'red', None, None)
2.36173138527
Environment.act() [POST]: location: (3, 2), heading: (0, 1), action: None, reward: 1.93115415731
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 1, 't': 24, 'action': None, 'reward': 1.9311541573108317, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.93)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 425
\-------------------------
Environment.reset(): Trial set up with start = (6, 6), destination = (3, 2), deadline = 25
0.198890670441
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1989; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'left', 'left')
1.71270811971
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: right, reward: 2.91464906601
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'left', 'left'), 'deadline': 25, 't': 0, 'action': 'right', 'reward': 2.914649066013329, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.91)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, 'left')
1.00580377693
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 2.3288778439
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 24, 't': 1, 'action': None, 'reward': 2.3288778439040474, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.33)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, 'left')
1.66734081042
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.12758374709
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 23, 't': 2, 'action': None, 'reward': 1.1275837470931598, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.13)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'right', 'left')
1.12298348196
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.36905428236
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', 'left'), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.3690542823565308, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', 'left')
Agent properly idled at a red light. (rewarded 1.37)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
2.34238389918
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: None, reward: 1.47689576334
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 21, 't': 4, 'action': None, 'reward': 1.4768957633395372, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.48)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', 'left')
2.36076927213
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 1.39729241712
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', 'left'), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 1.3972924171178345, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', 'left')
Agent followed the waypoint forward. (rewarded 1.40)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 1.52024951801
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 6, 'action': 'right', 'reward': 1.5202495180120694, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent drove right instead of forward. (rewarded 1.52)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', 'forward', None)
1.28317064663
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 2.67460435464
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.6746043546409206, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.67)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', 'forward', None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: -0.021210241119
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 17, 't': 8, 'action': 'right', 'reward': -0.021210241118995943, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent drove right instead of left. (rewarded -0.02)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'red', None, None)
2.05273587804
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.37162046717
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 16, 't': 9, 'action': None, 'reward': 1.3716204671686647, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.37)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'red', None, None)
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.21450367484
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 15, 't': 10, 'action': None, 'reward': 1.2145036748421483, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.21)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'green', 'right', 'right')
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 2.2952970349
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'right'), 'deadline': 14, 't': 11, 'action': 'right', 'reward': 2.295297034902222, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'right')
Agent followed the waypoint right. (rewarded 2.30)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'green', 'forward', None)
2.18612190444
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 1.4700213696
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.4700213696040583, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.47)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 2.33703223988
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 12, 't': 13, 'action': None, 'reward': 2.3370322398766916, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 2.34)
44% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('forward', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 0.84412874962
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', None), 'deadline': 11, 't': 14, 'action': 'right', 'reward': 0.8441287496200452, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', None)
Agent drove right instead of forward. (rewarded 0.84)
40% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', None)
1.97888750063
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 1.71699740054
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 15, 'action': None, 'reward': 1.7169974005410318, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.72)
36% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'red', 'forward', 'forward')
1.76669131322
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: None, reward: 0.894622395723
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 9, 't': 16, 'action': None, 'reward': 0.894622395723154, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 0.89)
32% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'forward', None)
0.762711875952
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 0.413965732683
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 8, 't': 17, 'action': 'right', 'reward': 0.4139657326825825, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.41)
28% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'red', None, None)
1.46334092372
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 2.46093605466
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 18, 'action': None, 'reward': 2.4609360546578634, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.46)
24% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'green', None, None)
2.01998663559
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 0.767611867921
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 6, 't': 19, 'action': 'right', 'reward': 0.7676118679207884, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 0.77)
20% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('right', 'green', 'right', 'left')
0.852759445027
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 0.654228730028
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'left'), 'deadline': 5, 't': 20, 'action': 'right', 'reward': 0.6542287300276843, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'left')
Agent followed the waypoint right. (rewarded 0.65)
16% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('forward', 'red', 'left', 'left')
1.88212834571
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 0.833786797008
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'left'), 'deadline': 4, 't': 21, 'action': None, 'reward': 0.8337867970083024, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'left')
Agent properly idled at a red light. (rewarded 0.83)
12% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: -9.52685728807
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 3, 't': 22, 'action': 'forward', 'reward': -9.52685728807086, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.53)
8% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('forward', 'green', 'left', None)
2.37478135547
Environment.act() [POST]: location: (3, 6), heading: (-1, 0), action: forward, reward: 1.71007780847
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 2, 't': 23, 'action': 'forward', 'reward': 1.71007780847409, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.71)
4% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('left', 'green', 'forward', None)
0.588338804317
Environment.act() [POST]: location: (3, 5), heading: (0, -1), action: right, reward: 1.05566743402
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 1, 't': 24, 'action': 'right', 'reward': 1.0556674340221177, 'waypoint': 'left'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 1.06)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 426
\-------------------------
Environment.reset(): Trial set up with start = (6, 2), destination = (1, 3), deadline = 20
0.198136320067
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1981; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'forward', 'left')
1.64201056183
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 1.86452977631
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 20, 't': 0, 'action': None, 'reward': 1.8645297763114932, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.86)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', 'left')
1.75327016907
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 1.27878803934
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'left'), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.2787880393390303, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.28)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', 'forward')
1.74499937569
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 2.34489866226
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.344898662264127, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 2.34)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', 'forward')
2.04494901897
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: None, reward: 1.11474585203
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'forward'), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.1147458520341733, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.11)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'right', 'right')
Environment.act() [POST]: location: (6, 2), heading: (0, -1), action: forward, reward: -10.6422250709
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'right', 'right'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': -10.642225070949591, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', 'right')
Agent attempted driving forward through a red light. (rewarded -10.64)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'forward', None)
1.82807163702
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: right, reward: 1.49057918539
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 1.490579185394851, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', None)
Agent followed the waypoint right. (rewarded 1.49)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', None, 'forward')
1.18453501628
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 0.922337202186
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 14, 't': 6, 'action': None, 'reward': 0.9223372021857572, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 0.92)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'left', None)
1.90963983126
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 1.4734465029
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.4734465029025807, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.47)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'left', None)
1.69154316708
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 2.46023127426
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 12, 't': 8, 'action': None, 'reward': 2.460231274263014, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.46)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', None)
2.07588722067
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 2.63092388745
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.6309238874464995, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.63)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', 'left', None)
2.35340555406
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: None, reward: 1.29094987411
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.290949874105687, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.29)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'green', 'left', None)
2.04242958197
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 1.18459451519
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 9, 't': 11, 'action': 'forward', 'reward': 1.1845945151912445, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.18)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'green', 'forward', 'left')
2.06860893852
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: 2.37202958196
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'left'), 'deadline': 8, 't': 12, 'action': 'forward', 'reward': 2.3720295819604598, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'left')
Agent followed the waypoint forward. (rewarded 2.37)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'green', 'right', 'forward')
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: None, reward: -4.18426465119
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 1, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 7, 't': 13, 'action': None, 'reward': -4.184264651185922, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent idled at a green light with no oncoming traffic. (rewarded -4.18)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'green', 'right', 'forward')
2.0339136174
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (1, 3), heading: (0, 1), action: right, reward: 0.640515579257
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'right', 'forward'), 'deadline': 6, 't': 14, 'action': 'right', 'reward': 0.6405155792568786, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'right', 'forward')
Agent followed the waypoint right. (rewarded 0.64)
25% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 427
\-------------------------
Environment.reset(): Trial set up with start = (6, 2), destination = (2, 5), deadline = 35
0.197384830784
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1974; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'left')
1.99307276339
Environment.act() [POST]: location: (7, 2), heading: (1, 0), action: right, reward: 2.62645524168
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 35, 't': 0, 'action': 'right', 'reward': 2.6264552416778573, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.63)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.4372396717
Environment.act() [POST]: location: (8, 2), heading: (1, 0), action: forward, reward: 2.57546848723
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 34, 't': 1, 'action': 'forward', 'reward': 2.57546848722537, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.58)
94% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', 'right', 'forward')
1.47656149323
Environment.act() [POST]: location: (1, 2), heading: (1, 0), action: forward, reward: 1.45860170051
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', 'forward'), 'deadline': 33, 't': 2, 'action': 'forward', 'reward': 1.4586017005064311, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', 'forward')
Agent followed the waypoint forward. (rewarded 1.46)
91% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, 'left')
1.43131044428
Environment.act() [POST]: location: (2, 2), heading: (1, 0), action: forward, reward: 2.69420679709
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 32, 't': 3, 'action': 'forward', 'reward': 2.694206797093784, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.69)
89% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'green', None, 'forward')
2.20601090389
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: left, reward: 2.49906101576
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'forward'), 'deadline': 31, 't': 4, 'action': 'left', 'reward': 2.499061015755503, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'forward')
Agent followed the waypoint left. (rewarded 2.50)
86% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'left', None)
1.82217771408
Environment.act() [POST]: location: (2, 7), heading: (0, -1), action: None, reward: 1.51285095031
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 30, 't': 5, 'action': None, 'reward': 1.5128509503072074, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.51)
83% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'left', None)
1.61351204858
Environment.act() [POST]: location: (2, 6), heading: (0, -1), action: forward, reward: 2.11175142349
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 29, 't': 6, 'action': 'forward', 'reward': 2.111751423487657, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.11)
80% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'green', 'left', None)
1.86263173603
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 5), heading: (0, -1), action: forward, reward: 2.61328875898
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 28, 't': 7, 'action': 'forward', 'reward': 2.6132887589793734, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.61)
77% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 428
\-------------------------
Environment.reset(): Trial set up with start = (8, 2), destination = (4, 3), deadline = 25
0.196636191742
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1966; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: 1.49378889299
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 25, 't': 0, 'action': 'forward', 'reward': 1.4937888929944543, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent drove forward instead of right. (rewarded 1.49)
96% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: forward, reward: -9.27197559545
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 24, 't': 1, 'action': 'forward', 'reward': -9.27197559544675, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent attempted driving forward through a red light. (rewarded -9.27)
92% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
1.94092115243
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 2.69157520676
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 23, 't': 2, 'action': None, 'reward': 2.69157520676026, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.69)
88% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
2.3162481796
Environment.act() [POST]: location: (8, 7), heading: (0, -1), action: None, reward: 1.33654760076
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 22, 't': 3, 'action': None, 'reward': 1.3365476007600736, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.34)
84% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'green', 'forward', 'forward')
1.747462566
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: right, reward: 2.89973622178
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 21, 't': 4, 'action': 'right', 'reward': 2.8997362217775087, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 2.90)
80% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'forward', None)
1.27118308821
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 2.70552932881
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 20, 't': 5, 'action': 'forward', 'reward': 2.705529328813228, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.71)
76% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: forward, reward: 2.54683421255
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 6, 'action': 'forward', 'reward': 2.5468342125539634, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.55)
72% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', None, 'forward')
1.05343610923
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 2.3484424078
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 18, 't': 7, 'action': None, 'reward': 2.3484424078033648, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 2.35)
68% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', None, 'left')
1.39746227876
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: None, reward: 2.07232094579
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 17, 't': 8, 'action': None, 'reward': 2.072320945787164, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.07)
64% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'red', 'left', 'right')
Environment.act() [POST]: location: (3, 7), heading: (1, 0), action: left, reward: -10.0993940507
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': 'right'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 16, 't': 9, 'action': 'left', 'reward': -10.099394050660784, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent attempted driving left through a red light. (rewarded -10.10)
60% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'green', 'left', None)
2.23796024751
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: forward, reward: 2.34817504784
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 10, 'action': 'forward', 'reward': 2.3481750478399017, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.35)
56% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'left')
2.26222308778
Environment.act() [POST]: location: (4, 7), heading: (1, 0), action: None, reward: 1.03833612422
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 14, 't': 11, 'action': None, 'reward': 1.0383361242199127, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.04)
52% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, 'left')
1.69992233439
Environment.act() [POST]: location: (4, 2), heading: (0, 1), action: right, reward: 1.23951844952
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 13, 't': 12, 'action': 'right', 'reward': 1.23951844952081, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent followed the waypoint right. (rewarded 1.24)
48% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', None, None)
2.00635407946
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 3), heading: (0, 1), action: forward, reward: 1.32996226005
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 12, 't': 13, 'action': 'forward', 'reward': 1.3299622600476728, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.33)
44% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 429
\-------------------------
Environment.reset(): Trial set up with start = (6, 4), destination = (4, 2), deadline = 20
0.19589039213
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1959; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'green', None, None)
2.19272742165
Environment.act() [POST]: location: (5, 4), heading: (-1, 0), action: left, reward: 2.72306643656
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'left', 'reward': 2.7230664365614228, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 2.72)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, None)
1.66815816975
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: forward, reward: 2.46832632498
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 2.4683263249821152, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.47)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', 'forward', None)
1.82639789018
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 2.21415570688
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.2141557068798594, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 2.21)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'red', 'forward', None)
2.02027679853
Environment.act() [POST]: location: (4, 4), heading: (-1, 0), action: None, reward: 1.27504171306
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.275041713064967, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.28)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('right', 'red', 'forward', None)
Environment.act() [POST]: location: (4, 3), heading: (0, -1), action: right, reward: 2.53820458507
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 16, 't': 4, 'action': 'right', 'reward': 2.5382045850738884, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 2.54)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
2.29306764767
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (4, 2), heading: (0, -1), action: forward, reward: 1.06515163614
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 1.06515163614046, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.07)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 430
\-------------------------
Environment.reset(): Trial set up with start = (2, 3), destination = (7, 4), deadline = 20
0.195147421179
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1951; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'forward', 'forward')
2.32359939389
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: right, reward: 2.91266085132
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'forward', 'forward'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.9126608513178986, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'forward', 'forward')
Agent followed the waypoint right. (rewarded 2.91)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', None, None)
2.14644277129
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 2.16947135774
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 1, 'action': None, 'reward': 2.169471357735672, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.17)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', None, None)
2.15795706451
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 1.64015911521
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.6401591152136223, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.64)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', None, None)
1.89905808986
Environment.act() [POST]: location: (1, 3), heading: (-1, 0), action: None, reward: 2.42666227702
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 2.4266622770230533, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.43)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, None)
Environment.act() [POST]: location: (1, 4), heading: (0, 1), action: left, reward: 0.0509479933591
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 16, 't': 4, 'action': 'left', 'reward': 0.050947993359139354, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent drove left instead of forward. (rewarded 0.05)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('right', 'green', 'left', None)
Environment.act() [POST]: location: (8, 4), heading: (-1, 0), action: right, reward: 2.36881906413
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'right', 'reward': 2.368819064128216, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', None)
Agent followed the waypoint right. (rewarded 2.37)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', None)
2.26759521053
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (7, 4), heading: (-1, 0), action: forward, reward: 2.47792106965
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 2.477921069653826, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 2.48)
65% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 431
\-------------------------
Environment.reset(): Trial set up with start = (7, 2), destination = (2, 7), deadline = 20
0.19440726816
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1944; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'red', 'right', None)
2.23968271679
Environment.act() [POST]: location: (7, 7), heading: (0, -1), action: right, reward: 2.57450235124
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.574502351241985, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'right', None)
Agent followed the waypoint right. (rewarded 2.57)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('right', 'red', None, 'right')
2.06920170298
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: right, reward: 2.10149258539
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 2.1014925853853486, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent followed the waypoint right. (rewarded 2.10)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', None)
1.66751433219
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.96253896692
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.9625389669204878, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.96)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'red', 'left', None)
1.81502664956
Environment.act() [POST]: location: (8, 7), heading: (1, 0), action: None, reward: 1.13372407328
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 17, 't': 3, 'action': None, 'reward': 1.1337240732764478, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.13)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', 'left', None)
1.67910964191
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: 1.86743843449
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 1.8674384344859807, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.87)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'red', 'forward', 'left')
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: forward, reward: -10.1619359334
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': -10.161935933361084, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent attempted driving forward through a red light. (rewarded -10.16)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'red', 'forward', 'forward')
2.03663574199
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 1.98704496759
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'forward'), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.9870449675919684, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.99)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('forward', 'red', 'forward', 'left')
1.98358516883
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 1.0954107627
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'left'), 'deadline': 13, 't': 7, 'action': None, 'reward': 1.095410762696054, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'left')
Agent properly idled at a red light. (rewarded 1.10)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'forward', 'right')
1.67550382533
Environment.act() [POST]: location: (1, 7), heading: (1, 0), action: None, reward: 1.47663493715
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'forward', 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.4766349371521459, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 1.48)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('forward', 'green', 'right', None)
2.03299290825
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (2, 7), heading: (1, 0), action: forward, reward: 1.27116657504
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'right', None), 'deadline': 11, 't': 9, 'action': 'forward', 'reward': 1.2711665750424346, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'right', None)
Agent followed the waypoint forward. (rewarded 1.27)
50% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 432
\-------------------------
Environment.reset(): Trial set up with start = (7, 5), destination = (6, 2), deadline = 20
0.193669922385
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1937; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', 'left', 'left')
2.30976400253
Environment.act() [POST]: location: (6, 5), heading: (-1, 0), action: right, reward: 2.27787428725
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', 'left', 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.2778742872500217, 'waypoint': 'right'}
Agent previous state: ('right', 'green', 'left', 'left')
Agent followed the waypoint right. (rewarded 2.28)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'green', None, 'left')
2.01009441994
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: left, reward: 1.03876945983
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, 'left'), 'deadline': 19, 't': 1, 'action': 'left', 'reward': 1.038769459828367, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, 'left')
Agent followed the waypoint left. (rewarded 1.04)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'green', None, None)
Environment.act() [POST]: location: (6, 6), heading: (0, 1), action: None, reward: -5.58119483994
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': -5.581194839936144, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent idled at a green light with no oncoming traffic. (rewarded -5.58)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', None, None)
2.06824224737
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: forward, reward: 2.15840231543
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 2.158402315428803, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 2.16)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'green', None, 'left')
2.06275862069
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 2), heading: (0, 1), action: forward, reward: 2.16060510456
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 16, 't': 4, 'action': 'forward', 'reward': 2.1606051045609025, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent followed the waypoint forward. (rewarded 2.16)
75% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 433
\-------------------------
Environment.reset(): Trial set up with start = (7, 7), destination = (4, 2), deadline = 20
0.192935373208
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1929; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, 'left')
1.74017650343
Environment.act() [POST]: location: (6, 7), heading: (-1, 0), action: right, reward: 2.03613412723
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 2.036134127230539, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 2.04)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', None, 'left')
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: right, reward: 1.819254392
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, 'left'), 'deadline': 19, 't': 1, 'action': 'right', 'reward': 1.819254392000074, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, 'left')
Agent drove right instead of forward. (rewarded 1.82)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
2.01948204932
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 2.23215459959
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.232154599588786, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.23)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
2.12581832445
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 0.966307568905
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 17, 't': 3, 'action': None, 'reward': 0.9663075689051939, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.97)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', None, None)
Environment.act() [POST]: location: (6, 6), heading: (0, -1), action: None, reward: 2.53332378997
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.533323789968227, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.53)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', None, None)
2.4578969291
Environment.act() [POST]: location: (5, 6), heading: (-1, 0), action: left, reward: 1.9742022334
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 15, 't': 5, 'action': 'left', 'reward': 1.9742022333956848, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.97)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('forward', 'green', 'forward', 'forward')
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: forward, reward: 1.68514248846
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', 'forward'), 'deadline': 14, 't': 6, 'action': 'forward', 'reward': 1.685142488459005, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', 'forward')
Agent followed the waypoint forward. (rewarded 1.69)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('left', 'red', None, None)
2.03969336832
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 2.78280080391
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 13, 't': 7, 'action': None, 'reward': 2.782800803914223, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.78)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('left', 'red', None, 'right')
1.89294942383
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: None, reward: 1.61817855706
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'right'), 'deadline': 12, 't': 8, 'action': None, 'reward': 1.6181785570553666, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'right')
Agent properly idled at a red light. (rewarded 1.62)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'green', None, None)
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 0.0113906389142
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 11, 't': 9, 'action': 'right', 'reward': 0.011390638914192008, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent drove right instead of left. (rewarded 0.01)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('right', 'green', None, None)
1.39379925176
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 1.50105923127
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 10, 't': 10, 'action': 'right', 'reward': 1.501059231269237, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.50)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('right', 'red', None, 'forward')
2.3706409617
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.11380479177
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'forward'), 'deadline': 9, 't': 11, 'action': None, 'reward': 1.113804791771688, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.11)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('right', 'red', None, 'right')
2.08534714418
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.93418763704
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'right'), 'deadline': 8, 't': 12, 'action': 'right', 'reward': 1.9341876370431603, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'right')
Agent followed the waypoint right. (rewarded 1.93)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('right', 'red', None, None)
1.96213848919
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.24974143557
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 13, 'action': None, 'reward': 2.249741435572364, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.25)
30% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('right', 'red', None, None)
2.10593996238
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 0.725699408503
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 6, 't': 14, 'action': None, 'reward': 0.7256994085030695, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.73)
25% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('right', 'red', 'forward', 'right')
1.77861165695
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 2.40602655079
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', 'right'), 'deadline': 5, 't': 15, 'action': None, 'reward': 2.4060265507874936, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', 'right')
Agent properly idled at a red light. (rewarded 2.41)
20% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('right', 'red', 'forward', None)
1.83641499553
Environment.act() [POST]: location: (4, 6), heading: (-1, 0), action: right, reward: 1.03733137545
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'forward', None), 'deadline': 4, 't': 16, 'action': 'right', 'reward': 1.037331375445622, 'waypoint': 'right'}
Agent previous state: ('right', 'red', 'forward', None)
Agent followed the waypoint right. (rewarded 1.04)
15% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'green', 'right', None)
1.11364371897
Environment.act() [POST]: location: (4, 5), heading: (0, -1), action: right, reward: 0.222779041049
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'right', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'right', None), 'deadline': 3, 't': 17, 'action': 'right', 'reward': 0.22277904104918422, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'right', None)
Agent drove right instead of left. (rewarded 0.22)
10% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('right', 'green', None, None)
1.44742924151
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: right, reward: 1.01170288708
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 2, 't': 18, 'action': 'right', 'reward': 1.0117028870768217, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.01)
5% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('right', 'red', 'right', 'forward')
1.92812901787
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.49041162351
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', 'right', 'forward'), 'deadline': 1, 't': 19, 'action': None, 'reward': 1.4904116235125215, 'waypoint': 'right'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('right', 'red', 'right', 'forward')
Agent properly idled at a red light. (rewarded 1.49)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 434
\-------------------------
Environment.reset(): Trial set up with start = (4, 6), destination = (6, 2), deadline = 20
0.19220361002
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1922; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('forward', 'red', 'left', None)
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: forward, reward: -9.53467282662
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 2, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 20, 't': 0, 'action': 'forward', 'reward': -9.534672826620396, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent attempted driving forward through a red light. (rewarded -9.53)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'red', 'left', None)
1.47437536142
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 1.60447228057
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 19, 't': 1, 'action': None, 'reward': 1.6044722805674587, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.60)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('forward', 'red', 'left', 'right')
1.67813337525
Environment.act() [POST]: location: (4, 6), heading: (1, 0), action: None, reward: 2.12907359061
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', 'right'), 'deadline': 18, 't': 2, 'action': None, 'reward': 2.1290735906055636, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', 'right')
Agent properly idled at a red light. (rewarded 2.13)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('forward', 'green', 'left', None)
1.7732740382
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: forward, reward: 2.23207742264
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 17, 't': 3, 'action': 'forward', 'reward': 2.2320774226420905, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.23)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
1.53942382099
Environment.act() [POST]: location: (5, 6), heading: (1, 0), action: None, reward: 2.17981854835
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 2.17981854834818, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 2.18)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
2.00267573042
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: forward, reward: 2.50895267391
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.50895267390999, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.51)
70% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
1.41581968544
Environment.act() [POST]: location: (6, 6), heading: (1, 0), action: None, reward: 1.81104055679
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 14, 't': 6, 'action': None, 'reward': 1.8110405567880543, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.81)
65% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, None)
Environment.act() [POST]: location: (6, 7), heading: (0, 1), action: right, reward: 1.55807639651
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 13, 't': 7, 'action': 'right', 'reward': 1.558076396509309, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 1.56)
60% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('forward', 'red', 'right', None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: right, reward: 1.50520262109
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'right', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'right', None), 'deadline': 12, 't': 8, 'action': 'right', 'reward': 1.5052026210917213, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'right', None)
Agent drove right instead of forward. (rewarded 1.51)
55% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('left', 'red', None, 'left')
1.61516924364
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 2.04012762793
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 11, 't': 9, 'action': None, 'reward': 2.0401276279309926, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 2.04)
50% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('left', 'red', 'forward', None)
1.84794245059
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: 1.8126120488
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 10, 't': 10, 'action': None, 'reward': 1.8126120487995723, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.81)
45% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('left', 'green', 'forward', None)
Environment.act() [POST]: location: (5, 7), heading: (-1, 0), action: None, reward: -4.6141418823
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 1, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 9, 't': 11, 'action': None, 'reward': -4.614141882299082, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent idled at a green light with no oncoming traffic. (rewarded -4.61)
40% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('left', 'green', None, None)
2.21604958125
Environment.act() [POST]: location: (5, 2), heading: (0, 1), action: left, reward: 1.24896375475
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 8, 't': 12, 'action': 'left', 'reward': 1.248963754745893, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 1.25)
35% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('left', 'green', None, None)
1.732506668
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (6, 2), heading: (1, 0), action: left, reward: 0.689016903697
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', None, None), 'deadline': 7, 't': 13, 'action': 'left', 'reward': 0.6890169036974796, 'waypoint': 'left'}
Agent previous state: ('left', 'green', None, None)
Agent followed the waypoint left. (rewarded 0.69)
30% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 435
\-------------------------
Environment.reset(): Trial set up with start = (3, 5), destination = (5, 7), deadline = 20
0.191474622256
Simulating trial. . .
epsilon = 0.1915; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1915; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1915; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1915; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1915; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1915; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('right', 'green', None, None)
1.22956606429
Environment.act() [POST]: location: (4, 5), heading: (1, 0), action: right, reward: 1.14494775776
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, None), 'deadline': 20, 't': 0, 'action': 'right', 'reward': 1.14494775775875, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, None)
Agent followed the waypoint right. (rewarded 1.14)
95% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('forward', 'green', 'forward', None)
2.37275814009
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: forward, reward: 1.35610280862
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 19, 't': 1, 'action': 'forward', 'reward': 1.356102808618074, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent followed the waypoint forward. (rewarded 1.36)
90% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('right', 'red', None, None)
1.61343012112
Environment.act() [POST]: location: (5, 5), heading: (1, 0), action: None, reward: 1.55519599805
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 18, 't': 2, 'action': None, 'reward': 1.5551959980495382, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.56)
85% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('right', 'green', None, 'left')
1.88815531533
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: right, reward: 1.47678508348
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'left'), 'deadline': 17, 't': 3, 'action': 'right', 'reward': 1.4767850834812588, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'left')
Agent followed the waypoint right. (rewarded 1.48)
80% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('forward', 'red', 'left', None)
1.85962118467
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: None, reward: 1.15425456084
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', 'left', None), 'deadline': 16, 't': 4, 'action': None, 'reward': 1.1542545608404093, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.15)
75% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('forward', 'green', 'left', None)
2.25581420216
Environment.act(): Primary agent has reached destination!
Environment.act() [POST]: location: (5, 7), heading: (0, 1), action: forward, reward: 2.73269902505
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 15, 't': 5, 'action': 'forward', 'reward': 2.732699025046812, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 2.73)
70% of time remaining to reach destination.
Trial Completed!
Agent reached the destination.
/-------------------------
| Training trial 436
\-------------------------
Environment.reset(): Trial set up with start = (2, 5), destination = (5, 2), deadline = 30
0.190748399389
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
Simulating trial. . .
epsilon = 0.1907; alpha = 0.5000
/-------------------
| Step 0 Results
\-------------------
Environment.step(): t = 0
('left', 'red', None, 'left')
1.82764843578
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.97200892232
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 30, 't': 0, 'action': None, 'reward': 1.9720089223212847, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.97)
97% of time remaining to reach destination.
/-------------------
| Step 1 Results
\-------------------
Environment.step(): t = 1
('left', 'red', None, None)
2.41124708612
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.04184925866
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 29, 't': 1, 'action': None, 'reward': 1.0418492586574524, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.04)
93% of time remaining to reach destination.
/-------------------
| Step 2 Results
\-------------------
Environment.step(): t = 2
('left', 'red', None, None)
1.72654817239
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 2.41544213246
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 28, 't': 2, 'action': None, 'reward': 2.4154421324636086, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.42)
90% of time remaining to reach destination.
/-------------------
| Step 3 Results
\-------------------
Environment.step(): t = 3
('left', 'red', None, None)
2.07099515243
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.84642657167
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 27, 't': 3, 'action': None, 'reward': 1.846426571674804, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.85)
87% of time remaining to reach destination.
/-------------------
| Step 4 Results
\-------------------
Environment.step(): t = 4
('left', 'red', 'forward', 'forward')
1.33065685447
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.04144177792
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 26, 't': 4, 'action': None, 'reward': 1.0414417779196299, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.04)
83% of time remaining to reach destination.
/-------------------
| Step 5 Results
\-------------------
Environment.step(): t = 5
('left', 'green', 'forward', None)
0.82200311917
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 0.61181546722
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 25, 't': 5, 'action': 'right', 'reward': 0.6118154672202388, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.61)
80% of time remaining to reach destination.
/-------------------
| Step 6 Results
\-------------------
Environment.step(): t = 6
('right', 'red', None, None)
1.58431305958
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: None, reward: 2.91996146644
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 24, 't': 6, 'action': None, 'reward': 2.919961466437197, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.92)
77% of time remaining to reach destination.
/-------------------
| Step 7 Results
\-------------------
Environment.step(): t = 7
('right', 'red', None, 'left')
1.650279606
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: None, reward: 1.51497104148
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, 'left'), 'deadline': 23, 't': 7, 'action': None, 'reward': 1.5149710414836035, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 1.51)
73% of time remaining to reach destination.
/-------------------
| Step 8 Results
\-------------------
Environment.step(): t = 8
('right', 'red', None, None)
2.25213726301
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: None, reward: 2.36117324152
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 22, 't': 8, 'action': None, 'reward': 2.3611732415211586, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.36)
70% of time remaining to reach destination.
/-------------------
| Step 9 Results
\-------------------
Environment.step(): t = 9
('right', 'green', None, 'right')
1.42797521823
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: right, reward: 1.05549322997
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': 'right'}, 'violation': 0, 'light': 'green', 'state': ('right', 'green', None, 'right'), 'deadline': 21, 't': 9, 'action': 'right', 'reward': 1.055493229971152, 'waypoint': 'right'}
Agent previous state: ('right', 'green', None, 'right')
Agent followed the waypoint right. (rewarded 1.06)
67% of time remaining to reach destination.
/-------------------
| Step 10 Results
\-------------------
Environment.step(): t = 10
('forward', 'red', None, None)
2.16286018344
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 2.59784614243
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 20, 't': 10, 'action': None, 'reward': 2.597846142428316, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.60)
63% of time remaining to reach destination.
/-------------------
| Step 11 Results
\-------------------
Environment.step(): t = 11
('forward', 'red', None, None)
2.38035316294
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 1.26174488479
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 19, 't': 11, 'action': None, 'reward': 1.2617448847926396, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.26)
60% of time remaining to reach destination.
/-------------------
| Step 12 Results
\-------------------
Environment.step(): t = 12
('forward', 'red', None, None)
1.82104902386
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: None, reward: 1.03128827091
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, None), 'deadline': 18, 't': 12, 'action': None, 'reward': 1.03128827091079, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, None)
Agent properly idled at a red light. (rewarded 1.03)
57% of time remaining to reach destination.
/-------------------
| Step 13 Results
\-------------------
Environment.step(): t = 13
('forward', 'green', 'forward', None)
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: right, reward: 0.831328462304
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'forward', None), 'deadline': 17, 't': 13, 'action': 'right', 'reward': 0.8313284623043209, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'forward', None)
Agent drove right instead of forward. (rewarded 0.83)
53% of time remaining to reach destination.
/-------------------
| Step 14 Results
\-------------------
Environment.step(): t = 14
('left', 'red', 'forward', None)
1.83027724969
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.04040863828
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', None), 'deadline': 16, 't': 14, 'action': None, 'reward': 1.0404086382769053, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', None)
Agent properly idled at a red light. (rewarded 1.04)
50% of time remaining to reach destination.
/-------------------
| Step 15 Results
\-------------------
Environment.step(): t = 15
('left', 'red', 'forward', 'forward')
1.1860493162
Environment.act() [POST]: location: (3, 5), heading: (0, 1), action: None, reward: 1.44500239833
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'forward', 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'forward', 'forward'), 'deadline': 15, 't': 15, 'action': None, 'reward': 1.4450023983339357, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'forward', 'forward')
Agent properly idled at a red light. (rewarded 1.45)
47% of time remaining to reach destination.
/-------------------
| Step 16 Results
\-------------------
Environment.step(): t = 16
('left', 'green', 'forward', None)
0.716909293195
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: right, reward: 0.840811168158
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 14, 't': 16, 'action': 'right', 'reward': 0.8408111681576855, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.84)
43% of time remaining to reach destination.
/-------------------
| Step 17 Results
\-------------------
Environment.step(): t = 17
('left', 'red', None, 'forward')
1.37351022196
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.50989475992
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'forward'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'forward'), 'deadline': 13, 't': 17, 'action': None, 'reward': 1.5098947599223842, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'forward')
Agent properly idled at a red light. (rewarded 1.51)
40% of time remaining to reach destination.
/-------------------
| Step 18 Results
\-------------------
Environment.step(): t = 18
('left', 'red', None, 'left')
1.89982867905
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 0.822444113817
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'forward', 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, 'left'), 'deadline': 12, 't': 18, 'action': None, 'reward': 0.8224441138170762, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.82)
37% of time remaining to reach destination.
/-------------------
| Step 19 Results
\-------------------
Environment.step(): t = 19
('left', 'red', None, None)
1.95871086205
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 0.94989146477
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', None, None), 'deadline': 11, 't': 19, 'action': None, 'reward': 0.9498914647698158, 'waypoint': 'left'}
Agent previous state: ('left', 'red', None, None)
Agent properly idled at a red light. (rewarded 0.95)
33% of time remaining to reach destination.
/-------------------
| Step 20 Results
\-------------------
Environment.step(): t = 20
('left', 'red', 'left', None)
2.04803038422
Environment.act() [POST]: location: (2, 5), heading: (-1, 0), action: None, reward: 1.4960447939
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'red', 'state': ('left', 'red', 'left', None), 'deadline': 10, 't': 20, 'action': None, 'reward': 1.496044793897266, 'waypoint': 'left'}
Agent previous state: ('left', 'red', 'left', None)
Agent properly idled at a red light. (rewarded 1.50)
30% of time remaining to reach destination.
/-------------------
| Step 21 Results
\-------------------
Environment.step(): t = 21
('left', 'green', 'left', None)
1.43715584108
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: right, reward: 0.0452265262908
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': 'left', 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'left', None), 'deadline': 9, 't': 21, 'action': 'right', 'reward': 0.04522652629078594, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'left', None)
Agent drove right instead of left. (rewarded 0.05)
27% of time remaining to reach destination.
/-------------------
| Step 22 Results
\-------------------
Environment.step(): t = 22
('right', 'red', None, None)
2.30665525227
Environment.act() [POST]: location: (2, 4), heading: (0, -1), action: None, reward: 2.04583494127
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 8, 't': 22, 'action': None, 'reward': 2.045834941265171, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent properly idled at a red light. (rewarded 2.05)
23% of time remaining to reach destination.
/-------------------
| Step 23 Results
\-------------------
Environment.step(): t = 23
('right', 'red', None, None)
Environment.act() [POST]: location: (3, 4), heading: (1, 0), action: right, reward: 0.484158185559
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'right', 'left': None}, 'violation': 0, 'light': 'red', 'state': ('right', 'red', None, None), 'deadline': 7, 't': 23, 'action': 'right', 'reward': 0.4841581855589454, 'waypoint': 'right'}
Agent previous state: ('right', 'red', None, None)
Agent followed the waypoint right. (rewarded 0.48)
20% of time remaining to reach destination.
/-------------------
| Step 24 Results
\-------------------
Environment.step(): t = 24
('forward', 'green', None, None)
2.1133222814
Environment.act() [POST]: location: (4, 4), heading: (1, 0), action: forward, reward: 1.51234761854
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 6, 't': 24, 'action': 'forward', 'reward': 1.5123476185414935, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 1.51)
17% of time remaining to reach destination.
/-------------------
| Step 25 Results
\-------------------
Environment.step(): t = 25
('forward', 'green', 'left', None)
2.49425661361
Environment.act() [POST]: location: (5, 4), heading: (1, 0), action: forward, reward: 1.89479026308
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'left', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', 'left', None), 'deadline': 5, 't': 25, 'action': 'forward', 'reward': 1.8947902630805804, 'waypoint': 'forward'}
Agent previous state: ('forward', 'green', 'left', None)
Agent followed the waypoint forward. (rewarded 1.89)
13% of time remaining to reach destination.
/-------------------
| Step 26 Results
\-------------------
Environment.step(): t = 26
('left', 'green', 'forward', None)
0.778860230676
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: right, reward: 0.50447480716
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': 'forward', 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('left', 'green', 'forward', None), 'deadline': 4, 't': 26, 'action': 'right', 'reward': 0.5044748071595357, 'waypoint': 'left'}
Agent previous state: ('left', 'green', 'forward', None)
Agent drove right instead of left. (rewarded 0.50)
10% of time remaining to reach destination.
/-------------------
| Step 27 Results
\-------------------
Environment.step(): t = 27
('forward', 'red', None, 'forward')
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: forward, reward: -40.3800301411
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': 'left', 'left': 'forward'}, 'violation': 4, 'light': 'red', 'state': ('forward', 'red', None, 'forward'), 'deadline': 3, 't': 27, 'action': 'forward', 'reward': -40.38003014109241, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'forward')
Agent attempted driving forward through a red light with traffic and cause a major accident. (rewarded -40.38)
7% of time remaining to reach destination.
/-------------------
| Step 28 Results
\-------------------
Environment.step(): t = 28
('forward', 'red', None, 'left')
1.73489161227
Environment.act() [POST]: location: (5, 5), heading: (0, 1), action: None, reward: 0.476684594934
Environment.act(): Step data: {'inputs': {'light': 'red', 'oncoming': None, 'right': None, 'left': 'left'}, 'violation': 0, 'light': 'red', 'state': ('forward', 'red', None, 'left'), 'deadline': 2, 't': 28, 'action': None, 'reward': 0.4766845949340053, 'waypoint': 'forward'}
Agent previous state: ('forward', 'red', None, 'left')
Agent properly idled at a red light. (rewarded 0.48)
3% of time remaining to reach destination.
/-------------------
| Step 29 Results
\-------------------
Environment.step(): t = 29
('forward', 'green', None, None)
1.81283494997
Environment.act() [POST]: location: (5, 6), heading: (0, 1), action: forward, reward: 0.279153244402
Environment.act(): Step data: {'inputs': {'light': 'green', 'oncoming': None, 'right': None, 'left': None}, 'violation': 0, 'light': 'green', 'state': ('forward', 'green', None, None), 'deadline': 1, 't': 29, 'action': 'forward', 'reward': 0.279153244402379, 'waypoint': 'forward'}
Environment.step(): Primary agent ran out of time! Trial aborted.
Agent previous state: ('forward', 'green', None, None)
Agent followed the waypoint forward. (rewarded 0.28)
0% of time remaining to reach destination.
Trial Aborted!
Agent did not reach the destination.
/-------------------------
| Training trial 437
\-------------------------
Environment.reset(): Trial set up with start = (3, 2), destination = (4, 5), deadline = 20
0.190024930932
Simulating trial. . .
epsilon = 0.1900; alpha = 0.5000
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--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-5-6366afbc7d65> in <module>() ----> 1 get_ipython().system(u'python smartcab/agent.py') C:\Users\User\Anaconda2\lib\site-packages\IPython\core\interactiveshell.pyc in system_piped(self, cmd) 2212 # a non-None value would trigger :func:`sys.displayhook` calls. 2213 # Instead, we store the exit_code in user_ns. -> 2214 self.user_ns['_exit_code'] = system(self.var_expand(cmd, depth=1)) 2215 2216 def system_raw(self, cmd): C:\Users\User\Anaconda2\lib\site-packages\IPython\utils\_process_win32.pyc in system(cmd) 130 if path is not None: 131 cmd = '"pushd %s &&"%s' % (path, cmd) --> 132 return process_handler(cmd, _system_body) 133 134 def getoutput(cmd): C:\Users\User\Anaconda2\lib\site-packages\IPython\utils\_process_common.pyc in process_handler(cmd, callback, stderr) 77 78 try: ---> 79 out = callback(p) 80 except KeyboardInterrupt: 81 print('^C') C:\Users\User\Anaconda2\lib\site-packages\IPython\utils\_process_win32.pyc in _system_body(p) 97 for line in read_no_interrupt(p.stdout).splitlines(): 98 line = line.decode(enc, 'replace') ---> 99 print(line, file=sys.stdout) 100 for line in read_no_interrupt(p.stderr).splitlines(): 101 line = line.decode(enc, 'replace') C:\Users\User\Anaconda2\lib\site-packages\ipykernel\iostream.pyc in write(self, string) 315 316 is_child = (not self._is_master_process()) --> 317 self._buffer.write(string) 318 if is_child: 319 # newlines imply flush in subprocesses ValueError: I/O operation on closed file
In [6]:
# Load the 'sim_improved-learning' file from the improved Q-Learning simulation
vs.plot_trials('sim_improved-learning.csv')
Question 7¶
Using the visualization above that was produced from your improved Q-Learning simulation, provide a final analysis and make observations about the improved driving agent like in Question 6. Questions you should answer:
- What decaying function was used for epsilon (the exploration factor)?
- Approximately how many training trials were needed for your agent before begining testing?
- What epsilon-tolerance and alpha (learning rate) did you use? Why did you use them?
- How much improvement was made with this Q-Learner when compared to the default Q-Learner from the previous section?
- Would you say that the Q-Learner results show that your driving agent successfully learned an appropriate policy?
- Are you satisfied with the safety and reliability ratings of the Smartcab?
Answer:
We implemented this model with a state space of Waypoint, Inputs['Light'], Inputs['Oncoming'] and Inputs['left']. We had 96 state space. The full 384 feature state space was not feasible to learn from and our agent kept getting into an accident. To have 384 feature state space and perform as good, we would need a lot more trials.
Epsilon was decayed with exp(-0.0038 x t). We wanted to take about 1200 trials with tolerance 0.01. So solving exp(-a(1200)) = 0.01, we got a value of 0.0038 for the constant. We took about 1200 training trials before testing as we had intended to. Epsilon-tolerance was 0.01 and alpha was 0.5. An alpha of 0.5 gives equal weightage to the update the Q values with reward and current value. A tolerance of 0.01 was sufficient to attain the desired rating.
This Q learner has improved significantly from the previous one with 20 trials. Comparing to the previous panel of total bad actions and accidents, we can see that the number of bad actions and accidents drop significantly as the trials increase. The rate of reliability was consistently poor in our previous one while here, though there were some drops to 80% or 90%, most of the times it was at the 100% mark after 600 trials. Also, the average reward per action increased drastically and stayed in the positive region. In the previous Q learning, the average reward was still hovering up and down and it was still negative. Also in this algorithm we had used an exponentially decaying exploration factor while previously we had used a linear one. Also, we had a lot more trials for our agent to learn. This Q learning algorithm has attained a score of A+ for safety which is very vital. It has gotten A for reliability which is far better than our previous one.
Yes, it has learnt an appropriate policy for the features mentioned. However, in the real world, we would need to consider more factors such as pedestrians. I am satisfied with the safety and reliability ratings.
Define an Optimal Policy¶
Sometimes, the answer to the important question "what am I trying to get my agent to learn?" only has a theoretical answer and cannot be concretely described. Here, however, you can concretely define what it is the agent is trying to learn, and that is the U.S. right-of-way traffic laws. Since these laws are known information, you can further define, for each state the Smartcab is occupying, the optimal action for the driving agent based on these laws. In that case, we call the set of optimal state-action pairs an optimal policy. Hence, unlike some theoretical answers, it is clear whether the agent is acting "incorrectly" not only by the reward (penalty) it receives, but also by pure observation. If the agent drives through a red light, we both see it receive a negative reward but also know that it is not the correct behavior. This can be used to your advantage for verifying whether the policy your driving agent has learned is the correct one, or if it is a suboptimal policy.
Question 8¶
Provide a few examples (using the states you've defined) of what an optimal policy for this problem would look like. Afterwards, investigate the 'sim_improved-learning.txt' text file to see the results of your improved Q-Learning algorithm. For each state that has been recorded from the simulation, is the policy (the action with the highest value) correct for the given state? Are there any states where the policy is different than what would be expected from an optimal policy? Provide an example of a state and all state-action rewards recorded, and explain why it is the correct policy.
Answer:
For our state space of (waypoint, inputs['light'], inputs['oncoming'], inputs['left']), we expect the following actions,
- (right, red , inputs['oncoming'], forward) -> none, regardless of other values for 'oncoming'.
- (forward/left, red, inputs['oncoming'], inputs['left']) -> none, regardless of other values
- (right, red , inputs['oncoming'], left/right/none) -> right
- (forward, green, inputs['oncoming'], inputs['left']) -> forward,
- (right, green, inputs['oncoming'], inputs['left']) -> right,
- (left, green, forward/right/left, inputs['left']) -> none, oncoming vehicles have right of way.
- (left, green, none, inputs['left']) -> left.
There are a few incorrect policies. Namely,
('left', 'green', 'forward', 'left') It is safe but should have been none.
-- forward : 0.73
-- right : 0.13
-- None : -4.10
-- left : -18.77
('left', 'green', 'right', 'right') It is safe but should have been none
-- forward : 0.39
-- right : 0.00
-- None : -2.13
-- left : -10.14
('left', 'green', 'right', 'left') It is safe but should have been none
-- forward : 0.66
-- right : 0.26
-- None : -4.22
-- left : -15.46
('left', 'green', 'forward', 'right') It is safe but should have been none
-- forward : 0.69
-- right : 0.37
-- None : -4.90
-- left : -15.45
('left', 'green', 'forward', None) It is safe but should have been none
-- forward : 0.12
-- right : 1.20
-- None : -4.67
-- left : -19.76
('left', 'green', 'right', 'forward') It is safe but should have been none
-- forward : 0.00
-- right : 0.92
-- None : -3.83
-- left : 0.00
('right', 'red', None, None) We can go right but it is safe at none.
-- forward : -24.90
-- right : 1.66
-- None : 1.78
-- left : -14.01
('right', 'red', 'left', None) Also not bad in the interest of safety but it can go right
-- forward : -9.81
-- right : 0.92
-- None : 1.49
-- left : -28.90
('left', 'green', 'right', None) It is safe but should have been none
-- forward : 0.28
-- right : 0.90
-- None : -4.74
-- left : -19.79
('right', 'red', 'forward', 'right') It is safe but should have been going right
-- forward : -8.64
-- right : 0.00
-- None : 1.95
-- left : -7.67
('right', 'red', 'right', 'right') It is safe but should have been going right
-- forward : -7.96
-- right : 0.76
-- None : 2.05
-- left : -34.54
All of the decisions are good in terms of safety. Many a times, the agent turns away from its destination. Hence, the lower reliability rating. This suggests that the policy is not the most efficient but it does not cause any major accidents. In that sense, it is optimal but it does mean that our agent would take a longer route to reach its destination.
Optional: Future Rewards - Discount Factor, 'gamma'¶
Curiously, as part of the Q-Learning algorithm, you were asked to not use the discount factor, 'gamma' in the implementation. Including future rewards in the algorithm is used to aid in propogating positive rewards backwards from a future state to the current state. Essentially, if the driving agent is given the option to make several actions to arrive at different states, including future rewards will bias the agent towards states that could provide even more rewards. An example of this would be the driving agent moving towards a goal: With all actions and rewards equal, moving towards the goal would theoretically yield better rewards if there is an additional reward for reaching the goal. However, even though in this project, the driving agent is trying to reach a destination in the allotted time, including future rewards will not benefit the agent. In fact, if the agent were given many trials to learn, it could negatively affect Q-values!
Optional Question 9¶
There are two characteristics about the project that invalidate the use of future rewards in the Q-Learning algorithm. One characteristic has to do with the Smartcab itself, and the other has to do with the environment. Can you figure out what they are and why future rewards won't work for this project?
Answer:
The environment is non-deterministic in the sense that the future states of the traffic lights cannot be determined to get an estimate of optimal future value. Meaning to say that you won't know which state you will end up in the future given our state space. Furthermore, the destination and starting location changes with every trial. Thus a future value for a state will not be the same for each different trial. This would only mess up our learner.
The smartcab only gets information from the current intersection and not from future states. Hence, it is not possible to assign a value for Q(S_t,a_t) for future time periods. It does not have a global view of the environment like the distance to destination. It only loses time at each intersection.
So if we put future rewards into this, they will be incorrect and possibly random for the reasons mentioned above and it would not benefit the model.
Note: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to
File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.